Analyses
- Packages and functions
- Importing data and checking
- 2 - Estimate parameters from cxr package
- 2.1 - Estimate All replicates together
- 2.2 - Estimate each replicate separately
- 3 - Predicting coexistence (structural stability)
- 4 - Can we predict the outcome of species interactions?
- 5 - Simulations for figure 1
- 6 - Bootstrap differences between selection regimes
Packages and functions
library(ggplot2)
library(plyr)
library(dplyr)
library(car)
library(fitdistrplus)
library(tidyr)
library(tidyverse)
library(ggtext)
library(lme4)
library(lmerTest)
library(emmeans)
library(glmmTMB)
library(ggbreak)
library(nlme)
library(cxr)
library(MASS)
library(mvtnorm)
library(DescTools)
library(phia)
library(performance)
library(DHARMa)
library(effects)
library(cowplot)
library(ggeffects)
library(marginaleffects)
library(ggtext)
library(R2admb)
#library(glmmADMB)
library(Rfast2)
library(gridExtra)
library(RColorBrewer)
library(gamlss)
library(gamlss.dist)
library(gamlss.add)
library(LSAfun)
library(arm)
#install.packages("devtools")
#require(devtools)
#remotes::install_github("RadicalCommEcol/anisoFun")
#pak::pkg_install("RadicalCommEcol/anisoFun")
#library(anisoFun)
theme_ines<-theme(axis.text = element_text(size=14), axis.title = element_text(size=14, face="bold"), legend.text = element_text(size=12), strip.text = element_text(size=14), plot.title = element_text(size=14, face="bold"), panel.grid=element_line(colour="white"), panel.background = element_rect(fill="white") , axis.line = element_line(linewidth = 0.5, linetype = "solid",
colour = "black"), strip.background = element_rect(fill="white"))
save_plot<-function(dir, width=15, height=10, ...){
ggsave(dir, width = width, height = height, units = c("cm"))
}
Env<-c("No cadmium", "Cadmium")
names(Env)<-c("N", "Cd")
regimeTu<-c("Tu no cadmium", "Tu cadmium")
names(regimeTu)<-c("SR1", "SR2")
regimeTe<-c("Te no cadmium", "Te cadmium")
names(regimeTe)<-c("SR4", "SR5")
colors_comb<-brewer.pal(name = "Spectral", 4)Importing data and checking
Importing Competitive ability
setwd("./Repository/For_repository/")
getwd()## [1] "/Volumes/IF/Desktop/Project_manuscript/Coexistence_cadmium/Repository/For_repository"ca<-read.csv(file = "./Data/CompetitiveAbility Cd_G40_complete.csv", header=TRUE) # cdata from the competitive ability
str(ca) ## 'data.frame': 3680 obs. of 24 variables:
## $ Block : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Box : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Leaf : int 3 4 3 4 3 4 3 4 3 4 ...
## $ Disk : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Env : chr "N" "N" "Cd" "Cd" ...
## $ FocalSR : int 4 4 4 4 4 4 4 4 4 4 ...
## $ CompSR : int NA NA NA NA NA NA NA NA NA NA ...
## $ Dens : int 1 1 1 1 2 2 2 2 4 4 ...
## $ Type : chr "INTRA" "INTRA" "INTRA" "INTRA" ...
## $ Focalfemale : chr "Te" "Te" "Te" "Te" ...
## $ FocalDead : int 0 0 1 0 1 1 0 0 0 0 ...
## $ FocalDrowned : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FocalMissing : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NumbDeadComp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NumberOfEggs : int 11 21 3 9 15 16 17 11 58 24 ...
## $ NumberOfEggsBelow : int 0 0 0 0 0 0 0 0 0 0 ...
## $ TeMales : int 3 3 0 3 2 5 5 3 8 8 ...
## $ TeFemales : int 6 9 2 5 2 10 4 4 9 6 ...
## $ TeQuiescentfemales: int 0 0 0 0 0 0 3 0 3 4 ...
## $ TuMales : int NA NA NA NA NA NA NA NA NA NA ...
## $ TuFemales : int NA NA NA NA NA NA NA NA NA NA ...
## $ TuQuiescentfemales: int NA NA NA NA NA NA NA NA NA NA ...
## $ Observations : chr NA NA NA NA ...# Summary of the data to be sure that everything is ok!
summary(as.factor(ca$FocalSR))## 1 2 4 5
## 1000 800 940 940ca$Block2<-as.factor(ca$Block)
ca$Rep2<-as.factor(ca$Rep)
ca$Disk2<-as.factor(ca$Disk)
ca$Leaf2<-as.factor(ca$Leaf)
ca$Env2<-as.factor(ca$Env)
ca$FocalSR2<-as.factor(ca$FocalSR)
ca$CompSR2<-as.factor(ca$CompSR)
ca$Type2<-as.factor(ca$Type)
ca$Focal_Female2<-as.factor(ca$Focalfemale)
regimeTu<-c("Tu \ncontrol", "Tu evolved \n in cadmium")
names(regimeTu)<-c("SR1", "SR2")
regimeTe<-c("Te \n control", "Te evolved \n in cadmium")
names(regimeTe)<-c("SR4", "SR5")Creating columns that are needed
ca$Nr_Focal_Females_Tu_Alive_G0<-sapply(c(1:length(ca$Block)), function(x){
if(ca$Focalfemale[x]=="Tu"){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]-ca$FocalDead[x]-ca$FocalDrowned[x]-ca$FocalMissing[x]
}else
a<-1-ca$FocalDead[x]-ca$FocalDrowned[x]-ca$FocalMissing[x]
}else
a<-NA
})
ca$Nr_Focal_Females_Te_Alive_G0<-sapply(c(1:length(ca$Block)), function(x){
if(ca$Focalfemale[x]=="Te"){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]-ca$FocalDead[x]-ca$FocalDrowned[x]-ca$FocalMissing[x]
}else
a<-1-ca$FocalDead[x]-ca$FocalDrowned[x]-ca$FocalMissing[x]
}else
a<-NA
})
ca$Num_Comp_Tu_Alive_G0<-sapply(c(1:length(ca$Block)), function(x){
if(ca$Focalfemale[x]=="Te"){
if(ca$Type[x]=="INTER"){
a<-ca$Dens[x]-ca$NumbDeadComp[x]-1
}else
a<-NA
}else
a<-NA
})
ca$Num_Comp_Te_Alive_G0<-sapply(c(1:length(ca$Block)), function(x){
if(ca$Focalfemale[x]=="Tu"){
if(ca$Type[x]=="INTER"){
a<-ca$Dens[x]-ca$NumbDeadComp[x]-1
}else
a<-NA
}else
a<-NA
})
ca$Nr_Focal_Females_G0<-sapply(c(1:length(ca$Block)), function(x){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]
}else
a<-1
})
str(ca)## 'data.frame': 3680 obs. of 38 variables:
## $ Block : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Box : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Leaf : int 3 4 3 4 3 4 3 4 3 4 ...
## $ Disk : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Env : chr "N" "N" "Cd" "Cd" ...
## $ FocalSR : int 4 4 4 4 4 4 4 4 4 4 ...
## $ CompSR : int NA NA NA NA NA NA NA NA NA NA ...
## $ Dens : int 1 1 1 1 2 2 2 2 4 4 ...
## $ Type : chr "INTRA" "INTRA" "INTRA" "INTRA" ...
## $ Focalfemale : chr "Te" "Te" "Te" "Te" ...
## $ FocalDead : int 0 0 1 0 1 1 0 0 0 0 ...
## $ FocalDrowned : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FocalMissing : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NumbDeadComp : int 0 0 0 0 0 0 0 0 0 0 ...
## $ NumberOfEggs : int 11 21 3 9 15 16 17 11 58 24 ...
## $ NumberOfEggsBelow : int 0 0 0 0 0 0 0 0 0 0 ...
## $ TeMales : int 3 3 0 3 2 5 5 3 8 8 ...
## $ TeFemales : int 6 9 2 5 2 10 4 4 9 6 ...
## $ TeQuiescentfemales : int 0 0 0 0 0 0 3 0 3 4 ...
## $ TuMales : int NA NA NA NA NA NA NA NA NA NA ...
## $ TuFemales : int NA NA NA NA NA NA NA NA NA NA ...
## $ TuQuiescentfemales : int NA NA NA NA NA NA NA NA NA NA ...
## $ Observations : chr NA NA NA NA ...
## $ Block2 : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Disk2 : Factor w/ 16 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ Leaf2 : Factor w/ 2 levels "3","4": 1 2 1 2 1 2 1 2 1 2 ...
## $ Env2 : Factor w/ 2 levels "Cd","N": 2 2 1 1 2 2 1 1 2 2 ...
## $ FocalSR2 : Factor w/ 4 levels "1","2","4","5": 3 3 3 3 3 3 3 3 3 3 ...
## $ CompSR2 : Factor w/ 4 levels "1","2","4","5": NA NA NA NA NA NA NA NA NA NA ...
## $ Type2 : Factor w/ 2 levels "INTER","INTRA": 2 2 2 2 2 2 2 2 2 2 ...
## $ Focal_Female2 : Factor w/ 2 levels "Te","Tu": 1 1 1 1 1 1 1 1 1 1 ...
## $ Nr_Focal_Females_Tu_Alive_G0: num NA NA NA NA NA NA NA NA NA NA ...
## $ Nr_Focal_Females_Te_Alive_G0: num 1 1 0 1 1 1 2 2 4 4 ...
## $ Num_Comp_Tu_Alive_G0 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Num_Comp_Te_Alive_G0 : num NA NA NA NA NA NA NA NA NA NA ...
## $ Nr_Focal_Females_G0 : num 1 1 1 1 2 2 2 2 4 4 ...summary(ca)## Block Rep Box Leaf Disk
## Min. :1.00 Min. :1.000 Min. :1.0 Min. :3.0 Min. : 1.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:1.0 1st Qu.:3.0 1st Qu.: 4.000
## Median :2.00 Median :3.000 Median :2.0 Median :3.5 Median : 7.000
## Mean :2.47 Mean :3.087 Mean :1.8 Mean :3.5 Mean : 7.326
## 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:2.0 3rd Qu.:4.0 3rd Qu.:11.000
## Max. :4.00 Max. :5.000 Max. :3.0 Max. :4.0 Max. :16.000
##
## Env FocalSR CompSR Dens
## Length:3680 Min. :1.000 Min. :1.000 Min. : 1.000
## Class :character 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 2.000
## Mode :character Median :4.000 Median :3.000 Median : 4.000
## Mean :3.005 Mean :2.972 Mean : 4.886
## 3rd Qu.:5.000 3rd Qu.:4.250 3rd Qu.:10.000
## Max. :5.000 Max. :5.000 Max. :10.000
## NA's :1520
## Type Focalfemale FocalDead FocalDrowned
## Length:3680 Length:3680 Min. :0.0000 Min. : 0.0000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.: 0.0000
## Mode :character Mode :character Median :0.0000 Median : 0.0000
## Mean :0.2572 Mean : 0.2082
## 3rd Qu.:0.0000 3rd Qu.: 0.0000
## Max. :9.0000 Max. :10.0000
## NA's :111 NA's :111
## FocalMissing NumbDeadComp NumberOfEggs NumberOfEggsBelow
## Min. :0.0000 Min. :0.0000 Min. : 0.00 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 7.00 1st Qu.: 0.0000
## Median :0.0000 Median :0.0000 Median : 20.00 Median : 0.0000
## Mean :0.0241 Mean :0.5479 Mean : 34.12 Mean : 0.0642
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.: 47.00 3rd Qu.: 0.0000
## Max. :3.0000 Max. :9.0000 Max. :240.00 Max. :27.0000
## NA's :111 NA's :112 NA's :111 NA's :113
## TeMales TeFemales TeQuiescentfemales TuMales
## Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 1.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 1.000 Median : 3.000 Median : 0.00 Median : 0.000
## Mean : 2.566 Mean : 7.627 Mean : 1.69 Mean : 1.533
## 3rd Qu.: 3.000 3rd Qu.:10.000 3rd Qu.: 2.00 3rd Qu.: 2.000
## Max. :121.000 Max. :82.000 Max. :43.00 Max. :110.000
## NA's :809 NA's :808 NA's :808 NA's :892
## TuFemales TuQuiescentfemales Observations Block2 Rep2
## Min. : 0.000 Min. : 0.0000 Length:3680 1: 832 1:800
## 1st Qu.: 0.000 1st Qu.: 0.0000 Class :character 2:1248 2:480
## Median : 1.000 Median : 0.0000 Mode :character 3: 640 3:800
## Mean : 2.103 Mean : 0.9505 4: 960 4:800
## 3rd Qu.: 3.000 3rd Qu.: 1.0000 5:800
## Max. :67.000 Max. :31.0000
## NA's :892 NA's :892
## Disk2 Leaf2 Env2 FocalSR2 CompSR2 Type2
## 1 : 275 3:1840 Cd:1840 1:1000 1 : 600 INTER:2160
## 2 : 275 4:1840 N :1840 2: 800 2 : 480 INTRA:1520
## 3 : 275 4: 940 4 : 540
## 4 : 275 5: 940 5 : 540
## 5 : 275 NA's:1520
## 6 : 275
## (Other):2030
## Focal_Female2 Nr_Focal_Females_Tu_Alive_G0 Nr_Focal_Females_Te_Alive_G0
## Te:1880 Min. : 0.000 Min. :-2.000
## Tu:1800 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 1.000 Median : 1.000
## Mean : 1.592 Mean : 2.076
## 3rd Qu.: 1.000 3rd Qu.: 2.000
## Max. :10.000 Max. :10.000
## NA's :1922 NA's :1869
## Num_Comp_Tu_Alive_G0 Num_Comp_Te_Alive_G0 Nr_Focal_Females_G0
## Min. :-3.000 Min. :-1.000 Min. : 1.000
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.: 1.000
## Median : 2.000 Median : 3.000 Median : 1.000
## Mean : 2.958 Mean : 3.795 Mean : 2.342
## 3rd Qu.: 5.000 3rd Qu.: 7.000 3rd Qu.: 2.000
## Max. : 9.000 Max. : 9.000 Max. :10.000
## NA's :2649 NA's :2617which(ca$Num_Comp_Te_Alive_G0<0)## [1] 616which(ca$Num_Comp_Tu_Alive_G0<0)## [1] 1160which(ca$Nr_Focal_Females_Tu_Alive_G0<0)## integer(0)which(ca$Nr_Focal_Females_Te_Alive_G0<0)## [1] 824 3559ca<-ca[-c(which(ca$Num_Comp_Te_Alive_G0<0),which(ca$Num_Comp_Tu_Alive_G0<0), which(ca$Nr_Focal_Females_Te_Alive_G0<0) ),]
#Creating the columns with the correct number of competitors. For conspecifics its always the same as the density to calculate the growth rate. For heterospecifics its always 1 female with X competitors, and DensFocal2 its also to do the same for the conspecifics
ca$DensFocal<-sapply(c(1:dim(ca)[1]), function(x){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]
}else if(ca$Type[x]=="INTER"){
a<-1
}
a
})
ca$DensComp<-sapply(c(1:dim(ca)[1]), function(x){
if(ca$Type[x]=="INTRA"){
a<-0
}else if(ca$Type[x]=="INTER"){
a<-ca$Dens[x]-1
}
a
})
ca$DensFocal2<-sapply(c(1:dim(ca)[1]), function(x){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]-1
}else if(ca$Type[x]=="INTER"){
a<-1
}
a
})
ca$DensComp2<-sapply(c(1:dim(ca)[1]), function(x){
if(ca$Type[x]=="INTRA"){
a<-ca$Dens[x]-1
}else if(ca$Type[x]=="INTER"){
a<-ca$Dens[x]-1
}
a
})
ca$CompSR3<-sapply(c(1:dim(ca)[1]), function(x){
if(ca$Type[x]=="INTRA"){
a<-ca$FocalSR[x]
}else if(ca$Type[x]=="INTER"){
a<-ca$CompSR[x]
}
a
})
ca$CompSR3<-as.factor(ca$CompSR3)Estimate growth rate
ca[,c("Nr_Focal_Females_G0", "Dens", "Type")]## Nr_Focal_Females_G0 Dens Type
## 1 1 1 INTRA
## 2 1 1 INTRA
## 3 1 1 INTRA
## 4 1 1 INTRA
## 5 2 2 INTRA
## 6 2 2 INTRA
## 7 2 2 INTRA
## 8 2 2 INTRA
## 9 4 4 INTRA
## 10 4 4 INTRA
## 11 4 4 INTRA
## 12 4 4 INTRA
## 13 10 10 INTRA
## 14 10 10 INTRA
## 15 10 10 INTRA
## 16 10 10 INTRA
## 17 1 1 INTRA
## 18 1 1 INTRA
## 19 1 1 INTRA
## 20 1 1 INTRA
## 21 2 2 INTRA
## 22 2 2 INTRA
## 23 2 2 INTRA
## 24 2 2 INTRA
## 25 4 4 INTRA
## 26 4 4 INTRA
## 27 4 4 INTRA
## 28 4 4 INTRA
## 29 10 10 INTRA
## 30 10 10 INTRA
## 31 10 10 INTRA
## 32 10 10 INTRA
## 33 1 1 INTRA
## 34 1 1 INTRA
## 35 1 1 INTRA
## 36 1 1 INTRA
## 37 2 2 INTRA
## 38 2 2 INTRA
## 39 2 2 INTRA
## 40 2 2 INTRA
## 41 4 4 INTRA
## 42 4 4 INTRA
## 43 4 4 INTRA
## 44 4 4 INTRA
## 45 10 10 INTRA
## 46 10 10 INTRA
## 47 10 10 INTRA
## 48 10 10 INTRA
## 49 1 1 INTRA
## 50 1 1 INTRA
## 51 1 1 INTRA
## 52 1 1 INTRA
## 53 2 2 INTRA
## 54 2 2 INTRA
## 55 2 2 INTRA
## 56 2 2 INTRA
## 57 4 4 INTRA
## 58 4 4 INTRA
## 59 4 4 INTRA
## 60 4 4 INTRA
## 61 10 10 INTRA
## 62 10 10 INTRA
## 63 10 10 INTRA
## 64 10 10 INTRA
## 65 1 2 INTER
## 66 1 2 INTER
## 67 1 2 INTER
## 68 1 2 INTER
## 69 1 4 INTER
## 70 1 4 INTER
## 71 1 4 INTER
## 72 1 4 INTER
## 73 1 10 INTER
## 74 1 10 INTER
## 75 1 10 INTER
## 76 1 10 INTER
## 77 1 2 INTER
## 78 1 2 INTER
## 79 1 2 INTER
## 80 1 2 INTER
## 81 1 4 INTER
## 82 1 4 INTER
## 83 1 4 INTER
## 84 1 4 INTER
## 85 1 10 INTER
## 86 1 10 INTER
## 87 1 10 INTER
## 88 1 10 INTER
## 89 1 2 INTER
## 90 1 2 INTER
## 91 1 2 INTER
## 92 1 2 INTER
## 93 1 4 INTER
## 94 1 4 INTER
## 95 1 4 INTER
## 96 1 4 INTER
## 97 1 10 INTER
## 98 1 10 INTER
## 99 1 10 INTER
## 100 1 10 INTER
## 101 1 2 INTER
## 102 1 2 INTER
## 103 1 2 INTER
## 104 1 2 INTER
## 105 1 4 INTER
## 106 1 4 INTER
## 107 1 4 INTER
## 108 1 4 INTER
## 109 1 10 INTER
## 110 1 10 INTER
## 111 1 10 INTER
## 112 1 10 INTER
## 113 1 2 INTER
## 114 1 2 INTER
## 115 1 2 INTER
## 116 1 2 INTER
## 117 1 4 INTER
## 118 1 4 INTER
## 119 1 4 INTER
## 120 1 4 INTER
## 121 1 10 INTER
## 122 1 10 INTER
## 123 1 10 INTER
## 124 1 10 INTER
## 125 1 2 INTER
## 126 1 2 INTER
## 127 1 2 INTER
## 128 1 2 INTER
## 129 1 4 INTER
## 130 1 4 INTER
## 131 1 4 INTER
## 132 1 4 INTER
## 133 1 10 INTER
## 134 1 10 INTER
## 135 1 10 INTER
## 136 1 10 INTER
## 137 1 2 INTER
## 138 1 2 INTER
## 139 1 2 INTER
## 140 1 2 INTER
## 141 1 4 INTER
## 142 1 4 INTER
## 143 1 4 INTER
## 144 1 4 INTER
## 145 1 10 INTER
## 146 1 10 INTER
## 147 1 10 INTER
## 148 1 10 INTER
## 149 1 2 INTER
## 150 1 2 INTER
## 151 1 2 INTER
## 152 1 2 INTER
## 153 1 4 INTER
## 154 1 4 INTER
## 155 1 4 INTER
## 156 1 4 INTER
## 157 1 10 INTER
## 158 1 10 INTER
## 159 1 10 INTER
## 160 1 10 INTER
## 161 1 1 INTRA
## 162 1 1 INTRA
## 163 1 1 INTRA
## 164 1 1 INTRA
## 165 2 2 INTRA
## 166 2 2 INTRA
## 167 2 2 INTRA
## 168 2 2 INTRA
## 169 4 4 INTRA
## 170 4 4 INTRA
## 171 4 4 INTRA
## 172 4 4 INTRA
## 173 10 10 INTRA
## 174 10 10 INTRA
## 175 10 10 INTRA
## 176 10 10 INTRA
## 177 1 1 INTRA
## 178 1 1 INTRA
## 179 1 1 INTRA
## 180 1 1 INTRA
## 181 2 2 INTRA
## 182 2 2 INTRA
## 183 2 2 INTRA
## 184 2 2 INTRA
## 185 4 4 INTRA
## 186 4 4 INTRA
## 187 4 4 INTRA
## 188 4 4 INTRA
## 189 10 10 INTRA
## 190 10 10 INTRA
## 191 10 10 INTRA
## 192 10 10 INTRA
## 193 1 1 INTRA
## 194 1 1 INTRA
## 195 1 1 INTRA
## 196 1 1 INTRA
## 197 2 2 INTRA
## 198 2 2 INTRA
## 199 2 2 INTRA
## 200 2 2 INTRA
## 201 4 4 INTRA
## 202 4 4 INTRA
## 203 4 4 INTRA
## 204 4 4 INTRA
## 205 10 10 INTRA
## 206 10 10 INTRA
## 207 10 10 INTRA
## 208 10 10 INTRA
## 209 1 1 INTRA
## 210 1 1 INTRA
## 211 1 1 INTRA
## 212 1 1 INTRA
## 213 2 2 INTRA
## 214 2 2 INTRA
## 215 2 2 INTRA
## 216 2 2 INTRA
## 217 4 4 INTRA
## 218 4 4 INTRA
## 219 4 4 INTRA
## 220 4 4 INTRA
## 221 10 10 INTRA
## 222 10 10 INTRA
## 223 10 10 INTRA
## 224 10 10 INTRA
## 225 1 2 INTER
## 226 1 2 INTER
## 227 1 2 INTER
## 228 1 2 INTER
## 229 1 4 INTER
## 230 1 4 INTER
## 231 1 4 INTER
## 232 1 4 INTER
## 233 1 10 INTER
## 234 1 10 INTER
## 235 1 10 INTER
## 236 1 10 INTER
## 237 1 2 INTER
## 238 1 2 INTER
## 239 1 2 INTER
## 240 1 2 INTER
## 241 1 4 INTER
## 242 1 4 INTER
## 243 1 4 INTER
## 244 1 4 INTER
## 245 1 10 INTER
## 246 1 10 INTER
## 247 1 10 INTER
## 248 1 10 INTER
## 249 1 2 INTER
## 250 1 2 INTER
## 251 1 2 INTER
## 252 1 2 INTER
## 253 1 4 INTER
## 254 1 4 INTER
## 255 1 4 INTER
## 256 1 4 INTER
## 257 1 10 INTER
## 258 1 10 INTER
## 259 1 10 INTER
## 260 1 10 INTER
## 261 1 2 INTER
## 262 1 2 INTER
## 263 1 2 INTER
## 264 1 2 INTER
## 265 1 4 INTER
## 266 1 4 INTER
## 267 1 4 INTER
## 268 1 4 INTER
## 269 1 10 INTER
## 270 1 10 INTER
## 271 1 10 INTER
## 272 1 10 INTER
## 273 1 2 INTER
## 274 1 2 INTER
## 275 1 2 INTER
## 276 1 2 INTER
## 277 1 4 INTER
## 278 1 4 INTER
## 279 1 4 INTER
## 280 1 4 INTER
## 281 1 10 INTER
## 282 1 10 INTER
## 283 1 10 INTER
## 284 1 10 INTER
## 285 1 2 INTER
## 286 1 2 INTER
## 287 1 2 INTER
## 288 1 2 INTER
## 289 1 4 INTER
## 290 1 4 INTER
## 291 1 4 INTER
## 292 1 4 INTER
## 293 1 10 INTER
## 294 1 10 INTER
## 295 1 10 INTER
## 296 1 10 INTER
## 297 1 2 INTER
## 298 1 2 INTER
## 299 1 2 INTER
## 300 1 2 INTER
## 301 1 4 INTER
## 302 1 4 INTER
## 303 1 4 INTER
## 304 1 4 INTER
## 305 1 10 INTER
## 306 1 10 INTER
## 307 1 10 INTER
## 308 1 10 INTER
## 309 1 2 INTER
## 310 1 2 INTER
## 311 1 2 INTER
## 312 1 2 INTER
## 313 1 4 INTER
## 314 1 4 INTER
## 315 1 4 INTER
## 316 1 4 INTER
## 317 1 10 INTER
## 318 1 10 INTER
## 319 1 10 INTER
## 320 1 10 INTER
## 321 1 2 INTER
## 322 1 2 INTER
## 323 1 2 INTER
## 324 1 2 INTER
## 325 1 4 INTER
## 326 1 4 INTER
## 327 1 4 INTER
## 328 1 4 INTER
## 329 1 10 INTER
## 330 1 10 INTER
## 331 1 10 INTER
## 332 1 10 INTER
## 333 1 2 INTER
## 334 1 2 INTER
## 335 1 2 INTER
## 336 1 2 INTER
## 337 1 4 INTER
## 338 1 4 INTER
## 339 1 4 INTER
## 340 1 4 INTER
## 341 1 10 INTER
## 342 1 10 INTER
## 343 1 10 INTER
## 344 1 10 INTER
## 345 1 2 INTER
## 346 1 2 INTER
## 347 1 2 INTER
## 348 1 2 INTER
## 349 1 4 INTER
## 350 1 4 INTER
## 351 1 4 INTER
## 352 1 4 INTER
## 353 1 10 INTER
## 354 1 10 INTER
## 355 1 10 INTER
## 356 1 10 INTER
## 357 1 2 INTER
## 358 1 2 INTER
## 359 1 2 INTER
## 360 1 2 INTER
## 361 1 4 INTER
## 362 1 4 INTER
## 363 1 4 INTER
## 364 1 4 INTER
## 365 1 10 INTER
## 366 1 10 INTER
## 367 1 10 INTER
## 368 1 10 INTER
## 369 1 1 INTRA
## 370 1 1 INTRA
## 371 1 1 INTRA
## 372 1 1 INTRA
## 373 2 2 INTRA
## 374 2 2 INTRA
## 375 2 2 INTRA
## 376 2 2 INTRA
## 377 4 4 INTRA
## 378 4 4 INTRA
## 379 4 4 INTRA
## 380 4 4 INTRA
## 381 10 10 INTRA
## 382 10 10 INTRA
## 383 10 10 INTRA
## 384 10 10 INTRA
## 385 1 1 INTRA
## 386 1 1 INTRA
## 387 1 1 INTRA
## 388 1 1 INTRA
## 389 2 2 INTRA
## 390 2 2 INTRA
## 391 2 2 INTRA
## 392 2 2 INTRA
## 393 4 4 INTRA
## 394 4 4 INTRA
## 395 4 4 INTRA
## 396 4 4 INTRA
## 397 10 10 INTRA
## 398 10 10 INTRA
## 399 10 10 INTRA
## 400 10 10 INTRA
## 401 1 2 INTER
## 402 1 2 INTER
## 403 1 2 INTER
## 404 1 2 INTER
## 405 1 4 INTER
## 406 1 4 INTER
## 407 1 4 INTER
## 408 1 4 INTER
## 409 1 10 INTER
## 410 1 10 INTER
## 411 1 10 INTER
## 412 1 10 INTER
## 413 1 2 INTER
## 414 1 2 INTER
## 415 1 2 INTER
## 416 1 2 INTER
## 417 1 4 INTER
## 418 1 4 INTER
## 419 1 4 INTER
## 420 1 4 INTER
## 421 1 10 INTER
## 422 1 10 INTER
## 423 1 10 INTER
## 424 1 10 INTER
## 425 1 2 INTER
## 426 1 2 INTER
## 427 1 2 INTER
## 428 1 2 INTER
## 429 1 4 INTER
## 430 1 4 INTER
## 431 1 4 INTER
## 432 1 4 INTER
## 433 1 10 INTER
## 434 1 10 INTER
## 435 1 10 INTER
## 436 1 10 INTER
## 437 1 2 INTER
## 438 1 2 INTER
## 439 1 2 INTER
## 440 1 2 INTER
## 441 1 4 INTER
## 442 1 4 INTER
## 443 1 4 INTER
## 444 1 4 INTER
## 445 1 10 INTER
## 446 1 10 INTER
## 447 1 10 INTER
## 448 1 10 INTER
## 449 1 1 INTRA
## 450 1 1 INTRA
## 451 1 1 INTRA
## 452 1 1 INTRA
## 453 2 2 INTRA
## 454 2 2 INTRA
## 455 2 2 INTRA
## 456 2 2 INTRA
## 457 4 4 INTRA
## 458 4 4 INTRA
## 459 4 4 INTRA
## 460 4 4 INTRA
## 461 10 10 INTRA
## 462 10 10 INTRA
## 463 10 10 INTRA
## 464 10 10 INTRA
## 465 1 1 INTRA
## 466 1 1 INTRA
## 467 1 1 INTRA
## 468 1 1 INTRA
## 469 2 2 INTRA
## 470 2 2 INTRA
## 471 2 2 INTRA
## 472 2 2 INTRA
## 473 4 4 INTRA
## 474 4 4 INTRA
## 475 4 4 INTRA
## 476 4 4 INTRA
## 477 10 10 INTRA
## 478 10 10 INTRA
## 479 10 10 INTRA
## 480 10 10 INTRA
## 481 1 1 INTRA
## 482 1 1 INTRA
## 483 1 1 INTRA
## 484 1 1 INTRA
## 485 2 2 INTRA
## 486 2 2 INTRA
## 487 2 2 INTRA
## 488 2 2 INTRA
## 489 4 4 INTRA
## 490 4 4 INTRA
## 491 4 4 INTRA
## 492 4 4 INTRA
## 493 10 10 INTRA
## 494 10 10 INTRA
## 495 10 10 INTRA
## 496 10 10 INTRA
## 497 1 1 INTRA
## 498 1 1 INTRA
## 499 1 1 INTRA
## 500 1 1 INTRA
## 501 2 2 INTRA
## 502 2 2 INTRA
## 503 2 2 INTRA
## 504 2 2 INTRA
## 505 4 4 INTRA
## 506 4 4 INTRA
## 507 4 4 INTRA
## 508 4 4 INTRA
## 509 10 10 INTRA
## 510 10 10 INTRA
## 511 10 10 INTRA
## 512 10 10 INTRA
## 513 1 2 INTER
## 514 1 2 INTER
## 515 1 2 INTER
## 516 1 2 INTER
## 517 1 4 INTER
## 518 1 4 INTER
## 519 1 4 INTER
## 520 1 4 INTER
## 521 1 10 INTER
## 522 1 10 INTER
## 523 1 10 INTER
## 524 1 10 INTER
## 525 1 2 INTER
## 526 1 2 INTER
## 527 1 2 INTER
## 528 1 2 INTER
## 529 1 4 INTER
## 530 1 4 INTER
## 531 1 4 INTER
## 532 1 4 INTER
## 533 1 10 INTER
## 534 1 10 INTER
## 535 1 10 INTER
## 536 1 10 INTER
## 537 1 1 INTRA
## 538 1 1 INTRA
## 539 1 1 INTRA
## 540 1 1 INTRA
## 541 2 2 INTRA
## 542 2 2 INTRA
## 543 2 2 INTRA
## 544 2 2 INTRA
## 545 4 4 INTRA
## 546 4 4 INTRA
## 547 4 4 INTRA
## 548 4 4 INTRA
## 549 10 10 INTRA
## 550 10 10 INTRA
## 551 10 10 INTRA
## 552 10 10 INTRA
## 553 1 1 INTRA
## 554 1 1 INTRA
## 555 1 1 INTRA
## 556 1 1 INTRA
## 557 2 2 INTRA
## 558 2 2 INTRA
## 559 2 2 INTRA
## 560 2 2 INTRA
## 561 4 4 INTRA
## 562 4 4 INTRA
## 563 4 4 INTRA
## 564 4 4 INTRA
## 565 10 10 INTRA
## 566 10 10 INTRA
## 567 10 10 INTRA
## 568 10 10 INTRA
## 569 1 1 INTRA
## 570 1 1 INTRA
## 571 1 1 INTRA
## 572 1 1 INTRA
## 573 2 2 INTRA
## 574 2 2 INTRA
## 575 2 2 INTRA
## 576 2 2 INTRA
## 577 4 4 INTRA
## 578 4 4 INTRA
## 579 4 4 INTRA
## 580 4 4 INTRA
## 581 10 10 INTRA
## 582 10 10 INTRA
## 583 10 10 INTRA
## 584 10 10 INTRA
## 585 1 1 INTRA
## 586 1 1 INTRA
## 587 1 1 INTRA
## 588 1 1 INTRA
## 589 2 2 INTRA
## 590 2 2 INTRA
## 591 2 2 INTRA
## 592 2 2 INTRA
## 593 4 4 INTRA
## 594 4 4 INTRA
## 595 4 4 INTRA
## 596 4 4 INTRA
## 597 10 10 INTRA
## 598 10 10 INTRA
## 599 10 10 INTRA
## 600 10 10 INTRA
## 601 1 2 INTER
## 602 1 2 INTER
## 603 1 2 INTER
## 604 1 2 INTER
## 605 1 4 INTER
## 606 1 4 INTER
## 607 1 4 INTER
## 608 1 4 INTER
## 609 1 10 INTER
## 610 1 10 INTER
## 611 1 10 INTER
## 612 1 10 INTER
## 613 1 2 INTER
## 614 1 2 INTER
## 615 1 2 INTER
## 617 1 4 INTER
## 618 1 4 INTER
## 619 1 4 INTER
## 620 1 4 INTER
## 621 1 10 INTER
## 622 1 10 INTER
## 623 1 10 INTER
## 624 1 10 INTER
## 625 1 2 INTER
## 626 1 2 INTER
## 627 1 2 INTER
## 628 1 2 INTER
## 629 1 4 INTER
## 630 1 4 INTER
## 631 1 4 INTER
## 632 1 4 INTER
## 633 1 10 INTER
## 634 1 10 INTER
## 635 1 10 INTER
## 636 1 10 INTER
## 637 1 2 INTER
## 638 1 2 INTER
## 639 1 2 INTER
## 640 1 2 INTER
## 641 1 4 INTER
## 642 1 4 INTER
## 643 1 4 INTER
## 644 1 4 INTER
## 645 1 10 INTER
## 646 1 10 INTER
## 647 1 10 INTER
## 648 1 10 INTER
## 649 1 2 INTER
## 650 1 2 INTER
## 651 1 2 INTER
## 652 1 2 INTER
## 653 1 4 INTER
## 654 1 4 INTER
## 655 1 4 INTER
## 656 1 4 INTER
## 657 1 10 INTER
## 658 1 10 INTER
## 659 1 10 INTER
## 660 1 10 INTER
## 661 1 2 INTER
## 662 1 2 INTER
## 663 1 2 INTER
## 664 1 2 INTER
## 665 1 4 INTER
## 666 1 4 INTER
## 667 1 4 INTER
## 668 1 4 INTER
## 669 1 10 INTER
## 670 1 10 INTER
## 671 1 10 INTER
## 672 1 10 INTER
## 673 1 2 INTER
## 674 1 2 INTER
## 675 1 2 INTER
## 676 1 2 INTER
## 677 1 4 INTER
## 678 1 4 INTER
## 679 1 4 INTER
## 680 1 4 INTER
## 681 1 10 INTER
## 682 1 10 INTER
## 683 1 10 INTER
## 684 1 10 INTER
## 685 1 2 INTER
## 686 1 2 INTER
## 687 1 2 INTER
## 688 1 2 INTER
## 689 1 4 INTER
## 690 1 4 INTER
## 691 1 4 INTER
## 692 1 4 INTER
## 693 1 10 INTER
## 694 1 10 INTER
## 695 1 10 INTER
## 696 1 10 INTER
## 697 1 2 INTER
## 698 1 2 INTER
## 699 1 2 INTER
## 700 1 2 INTER
## 701 1 4 INTER
## 702 1 4 INTER
## 703 1 4 INTER
## 704 1 4 INTER
## 705 1 10 INTER
## 706 1 10 INTER
## 707 1 10 INTER
## 708 1 10 INTER
## 709 1 2 INTER
## 710 1 2 INTER
## 711 1 2 INTER
## 712 1 2 INTER
## 713 1 4 INTER
## 714 1 4 INTER
## 715 1 4 INTER
## 716 1 4 INTER
## 717 1 10 INTER
## 718 1 10 INTER
## 719 1 10 INTER
## 720 1 10 INTER
## 721 1 2 INTER
## 722 1 2 INTER
## 723 1 2 INTER
## 724 1 2 INTER
## 725 1 4 INTER
## 726 1 4 INTER
## 727 1 4 INTER
## 728 1 4 INTER
## 729 1 10 INTER
## 730 1 10 INTER
## 731 1 10 INTER
## 732 1 10 INTER
## 733 1 2 INTER
## 734 1 2 INTER
## 735 1 2 INTER
## 736 1 2 INTER
## 737 1 4 INTER
## 738 1 4 INTER
## 739 1 4 INTER
## 740 1 4 INTER
## 741 1 10 INTER
## 742 1 10 INTER
## 743 1 10 INTER
## 744 1 10 INTER
## 745 1 2 INTER
## 746 1 2 INTER
## 747 1 2 INTER
## 748 1 2 INTER
## 749 1 4 INTER
## 750 1 4 INTER
## 751 1 4 INTER
## 752 1 4 INTER
## 753 1 10 INTER
## 754 1 10 INTER
## 755 1 10 INTER
## 756 1 10 INTER
## 757 1 2 INTER
## 758 1 2 INTER
## 759 1 2 INTER
## 760 1 2 INTER
## 761 1 4 INTER
## 762 1 4 INTER
## 763 1 4 INTER
## 764 1 4 INTER
## 765 1 10 INTER
## 766 1 10 INTER
## 767 1 10 INTER
## 768 1 10 INTER
## 769 1 1 INTRA
## 770 1 1 INTRA
## 771 1 1 INTRA
## 772 1 1 INTRA
## 773 2 2 INTRA
## 774 2 2 INTRA
## 775 2 2 INTRA
## 776 2 2 INTRA
## 777 4 4 INTRA
## 778 4 4 INTRA
## 779 4 4 INTRA
## 780 4 4 INTRA
## 781 10 10 INTRA
## 782 10 10 INTRA
## 783 10 10 INTRA
## 784 10 10 INTRA
## 785 1 1 INTRA
## 786 1 1 INTRA
## 787 1 1 INTRA
## 788 1 1 INTRA
## 789 2 2 INTRA
## 790 2 2 INTRA
## 791 2 2 INTRA
## 792 2 2 INTRA
## 793 4 4 INTRA
## 794 4 4 INTRA
## 795 4 4 INTRA
## 796 4 4 INTRA
## 797 10 10 INTRA
## 798 10 10 INTRA
## 799 10 10 INTRA
## 800 10 10 INTRA
## 801 1 1 INTRA
## 802 1 1 INTRA
## 803 1 1 INTRA
## 804 1 1 INTRA
## 805 2 2 INTRA
## 806 2 2 INTRA
## 807 2 2 INTRA
## 808 2 2 INTRA
## 809 4 4 INTRA
## 810 4 4 INTRA
## 811 4 4 INTRA
## 812 4 4 INTRA
## 813 10 10 INTRA
## 814 10 10 INTRA
## 815 10 10 INTRA
## 816 10 10 INTRA
## 817 1 1 INTRA
## 818 1 1 INTRA
## 819 1 1 INTRA
## 820 1 1 INTRA
## 821 2 2 INTRA
## 822 2 2 INTRA
## 823 2 2 INTRA
## 825 4 4 INTRA
## 826 4 4 INTRA
## 827 4 4 INTRA
## 828 4 4 INTRA
## 829 10 10 INTRA
## 830 10 10 INTRA
## 831 10 10 INTRA
## 832 10 10 INTRA
## 833 1 2 INTER
## 834 1 2 INTER
## 835 1 2 INTER
## 836 1 2 INTER
## 837 1 4 INTER
## 838 1 4 INTER
## 839 1 4 INTER
## 840 1 4 INTER
## 841 1 10 INTER
## 842 1 10 INTER
## 843 1 10 INTER
## 844 1 10 INTER
## 845 1 2 INTER
## 846 1 2 INTER
## 847 1 2 INTER
## 848 1 2 INTER
## 849 1 4 INTER
## 850 1 4 INTER
## 851 1 4 INTER
## 852 1 4 INTER
## 853 1 10 INTER
## 854 1 10 INTER
## 855 1 10 INTER
## 856 1 10 INTER
## 857 1 2 INTER
## 858 1 2 INTER
## 859 1 2 INTER
## 860 1 2 INTER
## 861 1 4 INTER
## 862 1 4 INTER
## 863 1 4 INTER
## 864 1 4 INTER
## 865 1 10 INTER
## 866 1 10 INTER
## 867 1 10 INTER
## 868 1 10 INTER
## 869 1 2 INTER
## 870 1 2 INTER
## 871 1 2 INTER
## 872 1 2 INTER
## 873 1 4 INTER
## 874 1 4 INTER
## 875 1 4 INTER
## 876 1 4 INTER
## 877 1 10 INTER
## 878 1 10 INTER
## 879 1 10 INTER
## 880 1 10 INTER
## 881 1 1 INTRA
## 882 1 1 INTRA
## 883 1 1 INTRA
## 884 1 1 INTRA
## 885 2 2 INTRA
## 886 2 2 INTRA
## 887 2 2 INTRA
## 888 2 2 INTRA
## 889 4 4 INTRA
## 890 4 4 INTRA
## 891 4 4 INTRA
## 892 4 4 INTRA
## 893 10 10 INTRA
## 894 10 10 INTRA
## 895 10 10 INTRA
## 896 10 10 INTRA
## 897 1 1 INTRA
## 898 1 1 INTRA
## 899 1 1 INTRA
## 900 1 1 INTRA
## 901 2 2 INTRA
## 902 2 2 INTRA
## 903 2 2 INTRA
## 904 2 2 INTRA
## 905 4 4 INTRA
## 906 4 4 INTRA
## 907 4 4 INTRA
## 908 4 4 INTRA
## 909 10 10 INTRA
## 910 10 10 INTRA
## 911 10 10 INTRA
## 912 10 10 INTRA
## 913 1 2 INTER
## 914 1 2 INTER
## 915 1 2 INTER
## 916 1 2 INTER
## 917 1 4 INTER
## 918 1 4 INTER
## 919 1 4 INTER
## 920 1 4 INTER
## 921 1 10 INTER
## 922 1 10 INTER
## 923 1 10 INTER
## 924 1 10 INTER
## 925 1 2 INTER
## 926 1 2 INTER
## 927 1 2 INTER
## 928 1 2 INTER
## 929 1 4 INTER
## 930 1 4 INTER
## 931 1 4 INTER
## 932 1 4 INTER
## 933 1 10 INTER
## 934 1 10 INTER
## 935 1 10 INTER
## 936 1 10 INTER
## 937 1 1 INTRA
## 938 1 1 INTRA
## 939 1 1 INTRA
## 940 1 1 INTRA
## 941 2 2 INTRA
## 942 2 2 INTRA
## 943 2 2 INTRA
## 944 2 2 INTRA
## 945 4 4 INTRA
## 946 4 4 INTRA
## 947 4 4 INTRA
## 948 4 4 INTRA
## 949 10 10 INTRA
## 950 10 10 INTRA
## 951 10 10 INTRA
## 952 10 10 INTRA
## 953 1 1 INTRA
## 954 1 1 INTRA
## 955 1 1 INTRA
## 956 1 1 INTRA
## 957 2 2 INTRA
## 958 2 2 INTRA
## 959 2 2 INTRA
## 960 2 2 INTRA
## 961 4 4 INTRA
## 962 4 4 INTRA
## 963 4 4 INTRA
## 964 4 4 INTRA
## 965 10 10 INTRA
## 966 10 10 INTRA
## 967 10 10 INTRA
## 968 10 10 INTRA
## 969 1 1 INTRA
## 970 1 1 INTRA
## 971 1 1 INTRA
## 972 1 1 INTRA
## 973 2 2 INTRA
## 974 2 2 INTRA
## 975 2 2 INTRA
## 976 2 2 INTRA
## 977 4 4 INTRA
## 978 4 4 INTRA
## 979 4 4 INTRA
## 980 4 4 INTRA
## 981 10 10 INTRA
## 982 10 10 INTRA
## 983 10 10 INTRA
## 984 10 10 INTRA
## 985 1 1 INTRA
## 986 1 1 INTRA
## 987 1 1 INTRA
## 988 1 1 INTRA
## 989 2 2 INTRA
## 990 2 2 INTRA
## 991 2 2 INTRA
## 992 2 2 INTRA
## 993 4 4 INTRA
## 994 4 4 INTRA
## 995 4 4 INTRA
## 996 4 4 INTRA
## 997 10 10 INTRA
## 998 10 10 INTRA
## 999 10 10 INTRA
## 1000 10 10 INTRA
## 1001 1 2 INTER
## 1002 1 2 INTER
## 1003 1 2 INTER
## 1004 1 2 INTER
## 1005 1 4 INTER
## 1006 1 4 INTER
## 1007 1 4 INTER
## 1008 1 4 INTER
## 1009 1 10 INTER
## 1010 1 10 INTER
## 1011 1 10 INTER
## 1012 1 10 INTER
## 1013 1 2 INTER
## 1014 1 2 INTER
## 1015 1 2 INTER
## 1016 1 2 INTER
## 1017 1 4 INTER
## 1018 1 4 INTER
## 1019 1 4 INTER
## 1020 1 4 INTER
## 1021 1 10 INTER
## 1022 1 10 INTER
## 1023 1 10 INTER
## 1024 1 10 INTER
## 1025 1 2 INTER
## 1026 1 2 INTER
## 1027 1 2 INTER
## 1028 1 2 INTER
## 1029 1 4 INTER
## 1030 1 4 INTER
## 1031 1 4 INTER
## 1032 1 4 INTER
## 1033 1 10 INTER
## 1034 1 10 INTER
## 1035 1 10 INTER
## 1036 1 10 INTER
## 1037 1 2 INTER
## 1038 1 2 INTER
## 1039 1 2 INTER
## 1040 1 2 INTER
## 1041 1 4 INTER
## 1042 1 4 INTER
## 1043 1 4 INTER
## 1044 1 4 INTER
## 1045 1 10 INTER
## 1046 1 10 INTER
## 1047 1 10 INTER
## 1048 1 10 INTER
## 1049 1 1 INTRA
## 1050 1 1 INTRA
## 1051 1 1 INTRA
## 1052 1 1 INTRA
## 1053 2 2 INTRA
## 1054 2 2 INTRA
## 1055 2 2 INTRA
## 1056 2 2 INTRA
## 1057 4 4 INTRA
## 1058 4 4 INTRA
## 1059 4 4 INTRA
## 1060 4 4 INTRA
## 1061 10 10 INTRA
## 1062 10 10 INTRA
## 1063 10 10 INTRA
## 1064 10 10 INTRA
## 1065 1 1 INTRA
## 1066 1 1 INTRA
## 1067 1 1 INTRA
## 1068 1 1 INTRA
## 1069 2 2 INTRA
## 1070 2 2 INTRA
## 1071 2 2 INTRA
## 1072 2 2 INTRA
## 1073 4 4 INTRA
## 1074 4 4 INTRA
## 1075 4 4 INTRA
## 1076 4 4 INTRA
## 1077 10 10 INTRA
## 1078 10 10 INTRA
## 1079 10 10 INTRA
## 1080 10 10 INTRA
## 1081 1 2 INTER
## 1082 1 2 INTER
## 1083 1 2 INTER
## 1084 1 2 INTER
## 1085 1 4 INTER
## 1086 1 4 INTER
## 1087 1 4 INTER
## 1088 1 4 INTER
## 1089 1 10 INTER
## 1090 1 10 INTER
## 1091 1 10 INTER
## 1092 1 10 INTER
## 1093 1 2 INTER
## 1094 1 2 INTER
## 1095 1 2 INTER
## 1096 1 2 INTER
## 1097 1 4 INTER
## 1098 1 4 INTER
## 1099 1 4 INTER
## 1100 1 4 INTER
## 1101 1 10 INTER
## 1102 1 10 INTER
## 1103 1 10 INTER
## 1104 1 10 INTER
## 1105 1 2 INTER
## 1106 1 2 INTER
## 1107 1 2 INTER
## 1108 1 2 INTER
## 1109 1 4 INTER
## 1110 1 4 INTER
## 1111 1 4 INTER
## 1112 1 4 INTER
## 1113 1 10 INTER
## 1114 1 10 INTER
## 1115 1 10 INTER
## 1116 1 10 INTER
## 1117 1 2 INTER
## 1118 1 2 INTER
## 1119 1 2 INTER
## 1120 1 2 INTER
## 1121 1 4 INTER
## 1122 1 4 INTER
## 1123 1 4 INTER
## 1124 1 4 INTER
## 1125 1 10 INTER
## 1126 1 10 INTER
## 1127 1 10 INTER
## 1128 1 10 INTER
## 1129 1 2 INTER
## 1130 1 2 INTER
## 1131 1 2 INTER
## 1132 1 2 INTER
## 1133 1 4 INTER
## 1134 1 4 INTER
## 1135 1 4 INTER
## 1136 1 4 INTER
## 1137 1 10 INTER
## 1138 1 10 INTER
## 1139 1 10 INTER
## 1140 1 10 INTER
## 1141 1 2 INTER
## 1142 1 2 INTER
## 1143 1 2 INTER
## 1144 1 2 INTER
## 1145 1 4 INTER
## 1146 1 4 INTER
## 1147 1 4 INTER
## 1148 1 4 INTER
## 1149 1 10 INTER
## 1150 1 10 INTER
## 1151 1 10 INTER
## 1152 1 10 INTER
## 1153 1 2 INTER
## 1154 1 2 INTER
## 1155 1 2 INTER
## 1156 1 2 INTER
## 1157 1 4 INTER
## 1158 1 4 INTER
## 1159 1 4 INTER
## 1161 1 10 INTER
## 1162 1 10 INTER
## 1163 1 10 INTER
## 1164 1 10 INTER
## 1165 1 2 INTER
## 1166 1 2 INTER
## 1167 1 2 INTER
## 1168 1 2 INTER
## 1169 1 4 INTER
## 1170 1 4 INTER
## 1171 1 4 INTER
## 1172 1 4 INTER
## 1173 1 10 INTER
## 1174 1 10 INTER
## 1175 1 10 INTER
## 1176 1 10 INTER
## 1177 1 1 INTRA
## 1178 1 1 INTRA
## 1179 1 1 INTRA
## 1180 1 1 INTRA
## 1181 2 2 INTRA
## 1182 2 2 INTRA
## 1183 2 2 INTRA
## 1184 2 2 INTRA
## 1185 4 4 INTRA
## 1186 4 4 INTRA
## 1187 4 4 INTRA
## 1188 4 4 INTRA
## 1189 10 10 INTRA
## 1190 10 10 INTRA
## 1191 10 10 INTRA
## 1192 10 10 INTRA
## 1193 1 1 INTRA
## 1194 1 1 INTRA
## 1195 1 1 INTRA
## 1196 1 1 INTRA
## 1197 2 2 INTRA
## 1198 2 2 INTRA
## 1199 2 2 INTRA
## 1200 2 2 INTRA
## 1201 4 4 INTRA
## 1202 4 4 INTRA
## 1203 4 4 INTRA
## 1204 4 4 INTRA
## 1205 10 10 INTRA
## 1206 10 10 INTRA
## 1207 10 10 INTRA
## 1208 10 10 INTRA
## 1209 1 1 INTRA
## 1210 1 1 INTRA
## 1211 1 1 INTRA
## 1212 1 1 INTRA
## 1213 2 2 INTRA
## 1214 2 2 INTRA
## 1215 2 2 INTRA
## 1216 2 2 INTRA
## 1217 4 4 INTRA
## 1218 4 4 INTRA
## 1219 4 4 INTRA
## 1220 4 4 INTRA
## 1221 10 10 INTRA
## 1222 10 10 INTRA
## 1223 10 10 INTRA
## 1224 10 10 INTRA
## 1225 1 1 INTRA
## 1226 1 1 INTRA
## 1227 1 1 INTRA
## 1228 1 1 INTRA
## 1229 2 2 INTRA
## 1230 2 2 INTRA
## 1231 2 2 INTRA
## 1232 2 2 INTRA
## 1233 4 4 INTRA
## 1234 4 4 INTRA
## 1235 4 4 INTRA
## 1236 4 4 INTRA
## 1237 10 10 INTRA
## 1238 10 10 INTRA
## 1239 10 10 INTRA
## 1240 10 10 INTRA
## 1241 1 2 INTER
## 1242 1 2 INTER
## 1243 1 2 INTER
## 1244 1 2 INTER
## 1245 1 4 INTER
## 1246 1 4 INTER
## 1247 1 4 INTER
## 1248 1 4 INTER
## 1249 1 10 INTER
## 1250 1 10 INTER
## 1251 1 10 INTER
## 1252 1 10 INTER
## 1253 1 2 INTER
## 1254 1 2 INTER
## 1255 1 2 INTER
## 1256 1 2 INTER
## 1257 1 4 INTER
## 1258 1 4 INTER
## 1259 1 4 INTER
## 1260 1 4 INTER
## 1261 1 10 INTER
## 1262 1 10 INTER
## 1263 1 10 INTER
## 1264 1 10 INTER
## 1265 1 2 INTER
## 1266 1 2 INTER
## 1267 1 2 INTER
## 1268 1 2 INTER
## 1269 1 4 INTER
## 1270 1 4 INTER
## 1271 1 4 INTER
## 1272 1 4 INTER
## 1273 1 10 INTER
## 1274 1 10 INTER
## 1275 1 10 INTER
## 1276 1 10 INTER
## 1277 1 2 INTER
## 1278 1 2 INTER
## 1279 1 2 INTER
## 1280 1 2 INTER
## 1281 1 4 INTER
## 1282 1 4 INTER
## 1283 1 4 INTER
## 1284 1 4 INTER
## 1285 1 10 INTER
## 1286 1 10 INTER
## 1287 1 10 INTER
## 1288 1 10 INTER
## 1289 1 2 INTER
## 1290 1 2 INTER
## 1291 1 2 INTER
## 1292 1 2 INTER
## 1293 1 4 INTER
## 1294 1 4 INTER
## 1295 1 4 INTER
## 1296 1 4 INTER
## 1297 1 10 INTER
## 1298 1 10 INTER
## 1299 1 10 INTER
## 1300 1 10 INTER
## 1301 1 2 INTER
## 1302 1 2 INTER
## 1303 1 2 INTER
## 1304 1 2 INTER
## 1305 1 4 INTER
## 1306 1 4 INTER
## 1307 1 4 INTER
## 1308 1 4 INTER
## 1309 1 10 INTER
## 1310 1 10 INTER
## 1311 1 10 INTER
## 1312 1 10 INTER
## 1313 1 2 INTER
## 1314 1 2 INTER
## 1315 1 2 INTER
## 1316 1 2 INTER
## 1317 1 4 INTER
## 1318 1 4 INTER
## 1319 1 4 INTER
## 1320 1 4 INTER
## 1321 1 10 INTER
## 1322 1 10 INTER
## 1323 1 10 INTER
## 1324 1 10 INTER
## 1325 1 2 INTER
## 1326 1 2 INTER
## 1327 1 2 INTER
## 1328 1 2 INTER
## 1329 1 4 INTER
## 1330 1 4 INTER
## 1331 1 4 INTER
## 1332 1 4 INTER
## 1333 1 10 INTER
## 1334 1 10 INTER
## 1335 1 10 INTER
## 1336 1 10 INTER
## 1337 1 1 INTRA
## 1338 1 1 INTRA
## 1339 1 1 INTRA
## 1340 1 1 INTRA
## 1341 2 2 INTRA
## 1342 2 2 INTRA
## 1343 2 2 INTRA
## 1344 2 2 INTRA
## 1345 4 4 INTRA
## 1346 4 4 INTRA
## 1347 4 4 INTRA
## 1348 4 4 INTRA
## 1349 10 10 INTRA
## 1350 10 10 INTRA
## 1351 10 10 INTRA
## 1352 10 10 INTRA
## 1353 1 1 INTRA
## 1354 1 1 INTRA
## 1355 1 1 INTRA
## 1356 1 1 INTRA
## 1357 2 2 INTRA
## 1358 2 2 INTRA
## 1359 2 2 INTRA
## 1360 2 2 INTRA
## 1361 4 4 INTRA
## 1362 4 4 INTRA
## 1363 4 4 INTRA
## 1364 4 4 INTRA
## 1365 10 10 INTRA
## 1366 10 10 INTRA
## 1367 10 10 INTRA
## 1368 10 10 INTRA
## 1369 1 1 INTRA
## 1370 1 1 INTRA
## 1371 1 1 INTRA
## 1372 1 1 INTRA
## 1373 2 2 INTRA
## 1374 2 2 INTRA
## 1375 2 2 INTRA
## 1376 2 2 INTRA
## 1377 4 4 INTRA
## 1378 4 4 INTRA
## 1379 4 4 INTRA
## 1380 4 4 INTRA
## 1381 10 10 INTRA
## 1382 10 10 INTRA
## 1383 10 10 INTRA
## 1384 10 10 INTRA
## 1385 1 1 INTRA
## 1386 1 1 INTRA
## 1387 1 1 INTRA
## 1388 1 1 INTRA
## 1389 2 2 INTRA
## 1390 2 2 INTRA
## 1391 2 2 INTRA
## 1392 2 2 INTRA
## 1393 4 4 INTRA
## 1394 4 4 INTRA
## 1395 4 4 INTRA
## 1396 4 4 INTRA
## 1397 10 10 INTRA
## 1398 10 10 INTRA
## 1399 10 10 INTRA
## 1400 10 10 INTRA
## 1401 1 2 INTER
## 1402 1 2 INTER
## 1403 1 2 INTER
## 1404 1 2 INTER
## 1405 1 4 INTER
## 1406 1 4 INTER
## 1407 1 4 INTER
## 1408 1 4 INTER
## 1409 1 10 INTER
## 1410 1 10 INTER
## 1411 1 10 INTER
## 1412 1 10 INTER
## 1413 1 2 INTER
## 1414 1 2 INTER
## 1415 1 2 INTER
## 1416 1 2 INTER
## 1417 1 4 INTER
## 1418 1 4 INTER
## 1419 1 4 INTER
## 1420 1 4 INTER
## 1421 1 10 INTER
## 1422 1 10 INTER
## 1423 1 10 INTER
## 1424 1 10 INTER
## 1425 1 2 INTER
## 1426 1 2 INTER
## 1427 1 2 INTER
## 1428 1 2 INTER
## 1429 1 4 INTER
## 1430 1 4 INTER
## 1431 1 4 INTER
## 1432 1 4 INTER
## 1433 1 10 INTER
## 1434 1 10 INTER
## 1435 1 10 INTER
## 1436 1 10 INTER
## 1437 1 2 INTER
## 1438 1 2 INTER
## 1439 1 2 INTER
## 1440 1 2 INTER
## 1441 1 4 INTER
## 1442 1 4 INTER
## 1443 1 4 INTER
## 1444 1 4 INTER
## 1445 1 10 INTER
## 1446 1 10 INTER
## 1447 1 10 INTER
## 1448 1 10 INTER
## 1449 1 2 INTER
## 1450 1 2 INTER
## 1451 1 2 INTER
## 1452 1 2 INTER
## 1453 1 4 INTER
## 1454 1 4 INTER
## 1455 1 4 INTER
## 1456 1 4 INTER
## 1457 1 10 INTER
## 1458 1 10 INTER
## 1459 1 10 INTER
## 1460 1 10 INTER
## 1461 1 2 INTER
## 1462 1 2 INTER
## 1463 1 2 INTER
## 1464 1 2 INTER
## 1465 1 4 INTER
## 1466 1 4 INTER
## 1467 1 4 INTER
## 1468 1 4 INTER
## 1469 1 10 INTER
## 1470 1 10 INTER
## 1471 1 10 INTER
## 1472 1 10 INTER
## 1473 1 2 INTER
## 1474 1 2 INTER
## 1475 1 2 INTER
## 1476 1 2 INTER
## 1477 1 4 INTER
## 1478 1 4 INTER
## 1479 1 4 INTER
## 1480 1 4 INTER
## 1481 1 10 INTER
## 1482 1 10 INTER
## 1483 1 10 INTER
## 1484 1 10 INTER
## 1485 1 2 INTER
## 1486 1 2 INTER
## 1487 1 2 INTER
## 1488 1 2 INTER
## 1489 1 4 INTER
## 1490 1 4 INTER
## 1491 1 4 INTER
## 1492 1 4 INTER
## 1493 1 10 INTER
## 1494 1 10 INTER
## 1495 1 10 INTER
## 1496 1 10 INTER
## 1497 1 2 INTER
## 1498 1 2 INTER
## 1499 1 2 INTER
## 1500 1 2 INTER
## 1501 1 4 INTER
## 1502 1 4 INTER
## 1503 1 4 INTER
## 1504 1 4 INTER
## 1505 1 10 INTER
## 1506 1 10 INTER
## 1507 1 10 INTER
## 1508 1 10 INTER
## 1509 1 1 INTRA
## 1510 1 1 INTRA
## 1511 1 1 INTRA
## 1512 1 1 INTRA
## 1513 2 2 INTRA
## 1514 2 2 INTRA
## 1515 2 2 INTRA
## 1516 2 2 INTRA
## 1517 4 4 INTRA
## 1518 4 4 INTRA
## 1519 4 4 INTRA
## 1520 4 4 INTRA
## 1521 10 10 INTRA
## 1522 10 10 INTRA
## 1523 10 10 INTRA
## 1524 10 10 INTRA
## 1525 1 1 INTRA
## 1526 1 1 INTRA
## 1527 1 1 INTRA
## 1528 1 1 INTRA
## 1529 2 2 INTRA
## 1530 2 2 INTRA
## 1531 2 2 INTRA
## 1532 2 2 INTRA
## 1533 4 4 INTRA
## 1534 4 4 INTRA
## 1535 4 4 INTRA
## 1536 4 4 INTRA
## 1537 10 10 INTRA
## 1538 10 10 INTRA
## 1539 10 10 INTRA
## 1540 10 10 INTRA
## 1541 1 1 INTRA
## 1542 1 1 INTRA
## 1543 1 1 INTRA
## 1544 1 1 INTRA
## 1545 2 2 INTRA
## 1546 2 2 INTRA
## 1547 2 2 INTRA
## 1548 2 2 INTRA
## 1549 4 4 INTRA
## 1550 4 4 INTRA
## 1551 4 4 INTRA
## 1552 4 4 INTRA
## 1553 10 10 INTRA
## 1554 10 10 INTRA
## 1555 10 10 INTRA
## 1556 10 10 INTRA
## 1557 1 2 INTER
## 1558 1 2 INTER
## 1559 1 2 INTER
## 1560 1 2 INTER
## 1561 1 4 INTER
## 1562 1 4 INTER
## 1563 1 4 INTER
## 1564 1 4 INTER
## 1565 1 10 INTER
## 1566 1 10 INTER
## 1567 1 10 INTER
## 1568 1 10 INTER
## 1569 1 2 INTER
## 1570 1 2 INTER
## 1571 1 2 INTER
## 1572 1 2 INTER
## 1573 1 4 INTER
## 1574 1 4 INTER
## 1575 1 4 INTER
## 1576 1 4 INTER
## 1577 1 10 INTER
## 1578 1 10 INTER
## 1579 1 10 INTER
## 1580 1 10 INTER
## 1581 1 2 INTER
## 1582 1 2 INTER
## 1583 1 2 INTER
## 1584 1 2 INTER
## 1585 1 4 INTER
## 1586 1 4 INTER
## 1587 1 4 INTER
## 1588 1 4 INTER
## 1589 1 10 INTER
## 1590 1 10 INTER
## 1591 1 10 INTER
## 1592 1 10 INTER
## 1593 1 2 INTER
## 1594 1 2 INTER
## 1595 1 2 INTER
## 1596 1 2 INTER
## 1597 1 4 INTER
## 1598 1 4 INTER
## 1599 1 4 INTER
## 1600 1 4 INTER
## 1601 1 10 INTER
## 1602 1 10 INTER
## 1603 1 10 INTER
## 1604 1 10 INTER
## 1605 1 2 INTER
## 1606 1 2 INTER
## 1607 1 2 INTER
## 1608 1 2 INTER
## 1609 1 4 INTER
## 1610 1 4 INTER
## 1611 1 4 INTER
## 1612 1 4 INTER
## 1613 1 10 INTER
## 1614 1 10 INTER
## 1615 1 10 INTER
## 1616 1 10 INTER
## 1617 1 2 INTER
## 1618 1 2 INTER
## 1619 1 2 INTER
## 1620 1 2 INTER
## 1621 1 4 INTER
## 1622 1 4 INTER
## 1623 1 4 INTER
## 1624 1 4 INTER
## 1625 1 10 INTER
## 1626 1 10 INTER
## 1627 1 10 INTER
## 1628 1 10 INTER
## 1629 1 1 INTRA
## 1630 1 1 INTRA
## 1631 1 1 INTRA
## 1632 1 1 INTRA
## 1633 2 2 INTRA
## 1634 2 2 INTRA
## 1635 2 2 INTRA
## 1636 2 2 INTRA
## 1637 4 4 INTRA
## 1638 4 4 INTRA
## 1639 4 4 INTRA
## 1640 4 4 INTRA
## 1641 10 10 INTRA
## 1642 10 10 INTRA
## 1643 10 10 INTRA
## 1644 10 10 INTRA
## 1645 1 1 INTRA
## 1646 1 1 INTRA
## 1647 1 1 INTRA
## 1648 1 1 INTRA
## 1649 2 2 INTRA
## 1650 2 2 INTRA
## 1651 2 2 INTRA
## 1652 2 2 INTRA
## 1653 4 4 INTRA
## 1654 4 4 INTRA
## 1655 4 4 INTRA
## 1656 4 4 INTRA
## 1657 10 10 INTRA
## 1658 10 10 INTRA
## 1659 10 10 INTRA
## 1660 10 10 INTRA
## 1661 1 1 INTRA
## 1662 1 1 INTRA
## 1663 1 1 INTRA
## 1664 1 1 INTRA
## 1665 2 2 INTRA
## 1666 2 2 INTRA
## 1667 2 2 INTRA
## 1668 2 2 INTRA
## 1669 4 4 INTRA
## 1670 4 4 INTRA
## 1671 4 4 INTRA
## 1672 4 4 INTRA
## 1673 10 10 INTRA
## 1674 10 10 INTRA
## 1675 10 10 INTRA
## 1676 10 10 INTRA
## 1677 1 2 INTER
## 1678 1 2 INTER
## 1679 1 2 INTER
## 1680 1 2 INTER
## 1681 1 4 INTER
## 1682 1 4 INTER
## 1683 1 4 INTER
## 1684 1 4 INTER
## 1685 1 10 INTER
## 1686 1 10 INTER
## 1687 1 10 INTER
## 1688 1 10 INTER
## 1689 1 2 INTER
## 1690 1 2 INTER
## 1691 1 2 INTER
## 1692 1 2 INTER
## 1693 1 4 INTER
## 1694 1 4 INTER
## 1695 1 4 INTER
## 1696 1 4 INTER
## 1697 1 10 INTER
## 1698 1 10 INTER
## 1699 1 10 INTER
## 1700 1 10 INTER
## 1701 1 2 INTER
## 1702 1 2 INTER
## 1703 1 2 INTER
## 1704 1 2 INTER
## 1705 1 4 INTER
## 1706 1 4 INTER
## 1707 1 4 INTER
## 1708 1 4 INTER
## 1709 1 10 INTER
## 1710 1 10 INTER
## 1711 1 10 INTER
## 1712 1 10 INTER
## 1713 1 2 INTER
## 1714 1 2 INTER
## 1715 1 2 INTER
## 1716 1 2 INTER
## 1717 1 4 INTER
## 1718 1 4 INTER
## 1719 1 4 INTER
## 1720 1 4 INTER
## 1721 1 10 INTER
## 1722 1 10 INTER
## 1723 1 10 INTER
## 1724 1 10 INTER
## 1725 1 2 INTER
## 1726 1 2 INTER
## 1727 1 2 INTER
## 1728 1 2 INTER
## 1729 1 4 INTER
## 1730 1 4 INTER
## 1731 1 4 INTER
## 1732 1 4 INTER
## 1733 1 10 INTER
## 1734 1 10 INTER
## 1735 1 10 INTER
## 1736 1 10 INTER
## 1737 1 2 INTER
## 1738 1 2 INTER
## 1739 1 2 INTER
## 1740 1 2 INTER
## 1741 1 4 INTER
## 1742 1 4 INTER
## 1743 1 4 INTER
## 1744 1 4 INTER
## 1745 1 10 INTER
## 1746 1 10 INTER
## 1747 1 10 INTER
## 1748 1 10 INTER
## 1749 1 1 INTRA
## 1750 1 1 INTRA
## 1751 1 1 INTRA
## 1752 1 1 INTRA
## 1753 2 2 INTRA
## 1754 2 2 INTRA
## 1755 2 2 INTRA
## 1756 2 2 INTRA
## 1757 4 4 INTRA
## 1758 4 4 INTRA
## 1759 4 4 INTRA
## 1760 4 4 INTRA
## 1761 10 10 INTRA
## 1762 10 10 INTRA
## 1763 10 10 INTRA
## 1764 10 10 INTRA
## 1765 1 1 INTRA
## 1766 1 1 INTRA
## 1767 1 1 INTRA
## 1768 1 1 INTRA
## 1769 2 2 INTRA
## 1770 2 2 INTRA
## 1771 2 2 INTRA
## 1772 2 2 INTRA
## 1773 4 4 INTRA
## 1774 4 4 INTRA
## 1775 4 4 INTRA
## 1776 4 4 INTRA
## 1777 10 10 INTRA
## 1778 10 10 INTRA
## 1779 10 10 INTRA
## 1780 10 10 INTRA
## 1781 1 1 INTRA
## 1782 1 1 INTRA
## 1783 1 1 INTRA
## 1784 1 1 INTRA
## 1785 2 2 INTRA
## 1786 2 2 INTRA
## 1787 2 2 INTRA
## 1788 2 2 INTRA
## 1789 4 4 INTRA
## 1790 4 4 INTRA
## 1791 4 4 INTRA
## 1792 4 4 INTRA
## 1793 10 10 INTRA
## 1794 10 10 INTRA
## 1795 10 10 INTRA
## 1796 10 10 INTRA
## 1797 1 2 INTER
## 1798 1 2 INTER
## 1799 1 2 INTER
## 1800 1 2 INTER
## 1801 1 4 INTER
## 1802 1 4 INTER
## 1803 1 4 INTER
## 1804 1 4 INTER
## 1805 1 10 INTER
## 1806 1 10 INTER
## 1807 1 10 INTER
## 1808 1 10 INTER
## 1809 1 2 INTER
## 1810 1 2 INTER
## 1811 1 2 INTER
## 1812 1 2 INTER
## 1813 1 4 INTER
## 1814 1 4 INTER
## 1815 1 4 INTER
## 1816 1 4 INTER
## 1817 1 10 INTER
## 1818 1 10 INTER
## 1819 1 10 INTER
## 1820 1 10 INTER
## 1821 1 2 INTER
## 1822 1 2 INTER
## 1823 1 2 INTER
## 1824 1 2 INTER
## 1825 1 4 INTER
## 1826 1 4 INTER
## 1827 1 4 INTER
## 1828 1 4 INTER
## 1829 1 10 INTER
## 1830 1 10 INTER
## 1831 1 10 INTER
## 1832 1 10 INTER
## 1833 1 2 INTER
## 1834 1 2 INTER
## 1835 1 2 INTER
## 1836 1 2 INTER
## 1837 1 4 INTER
## 1838 1 4 INTER
## 1839 1 4 INTER
## 1840 1 4 INTER
## 1841 1 10 INTER
## 1842 1 10 INTER
## 1843 1 10 INTER
## 1844 1 10 INTER
## 1845 1 2 INTER
## 1846 1 2 INTER
## 1847 1 2 INTER
## 1848 1 2 INTER
## 1849 1 4 INTER
## 1850 1 4 INTER
## 1851 1 4 INTER
## 1852 1 4 INTER
## 1853 1 10 INTER
## 1854 1 10 INTER
## 1855 1 10 INTER
## 1856 1 10 INTER
## 1857 1 2 INTER
## 1858 1 2 INTER
## 1859 1 2 INTER
## 1860 1 2 INTER
## 1861 1 4 INTER
## 1862 1 4 INTER
## 1863 1 4 INTER
## 1864 1 4 INTER
## 1865 1 10 INTER
## 1866 1 10 INTER
## 1867 1 10 INTER
## 1868 1 10 INTER
## 1869 1 2 INTER
## 1870 1 2 INTER
## 1871 1 2 INTER
## 1872 1 2 INTER
## 1873 1 4 INTER
## 1874 1 4 INTER
## 1875 1 4 INTER
## 1876 1 4 INTER
## 1877 1 10 INTER
## 1878 1 10 INTER
## 1879 1 10 INTER
## 1880 1 10 INTER
## 1881 1 2 INTER
## 1882 1 2 INTER
## 1883 1 2 INTER
## 1884 1 2 INTER
## 1885 1 4 INTER
## 1886 1 4 INTER
## 1887 1 4 INTER
## 1888 1 4 INTER
## 1889 1 10 INTER
## 1890 1 10 INTER
## 1891 1 10 INTER
## 1892 1 10 INTER
## 1893 1 2 INTER
## 1894 1 2 INTER
## 1895 1 2 INTER
## 1896 1 2 INTER
## 1897 1 4 INTER
## 1898 1 4 INTER
## 1899 1 4 INTER
## 1900 1 4 INTER
## 1901 1 10 INTER
## 1902 1 10 INTER
## 1903 1 10 INTER
## 1904 1 10 INTER
## 1905 1 1 INTRA
## 1906 1 1 INTRA
## 1907 1 1 INTRA
## 1908 1 1 INTRA
## 1909 2 2 INTRA
## 1910 2 2 INTRA
## 1911 2 2 INTRA
## 1912 2 2 INTRA
## 1913 4 4 INTRA
## 1914 4 4 INTRA
## 1915 4 4 INTRA
## 1916 4 4 INTRA
## 1917 10 10 INTRA
## 1918 10 10 INTRA
## 1919 10 10 INTRA
## 1920 10 10 INTRA
## 1921 1 1 INTRA
## 1922 1 1 INTRA
## 1923 1 1 INTRA
## 1924 1 1 INTRA
## 1925 2 2 INTRA
## 1926 2 2 INTRA
## 1927 2 2 INTRA
## 1928 2 2 INTRA
## 1929 4 4 INTRA
## 1930 4 4 INTRA
## 1931 4 4 INTRA
## 1932 4 4 INTRA
## 1933 10 10 INTRA
## 1934 10 10 INTRA
## 1935 10 10 INTRA
## 1936 10 10 INTRA
## 1937 1 1 INTRA
## 1938 1 1 INTRA
## 1939 1 1 INTRA
## 1940 1 1 INTRA
## 1941 2 2 INTRA
## 1942 2 2 INTRA
## 1943 2 2 INTRA
## 1944 2 2 INTRA
## 1945 4 4 INTRA
## 1946 4 4 INTRA
## 1947 4 4 INTRA
## 1948 4 4 INTRA
## 1949 10 10 INTRA
## 1950 10 10 INTRA
## 1951 10 10 INTRA
## 1952 10 10 INTRA
## 1953 1 1 INTRA
## 1954 1 1 INTRA
## 1955 1 1 INTRA
## 1956 1 1 INTRA
## 1957 2 2 INTRA
## 1958 2 2 INTRA
## 1959 2 2 INTRA
## 1960 2 2 INTRA
## 1961 4 4 INTRA
## 1962 4 4 INTRA
## 1963 4 4 INTRA
## 1964 4 4 INTRA
## 1965 10 10 INTRA
## 1966 10 10 INTRA
## 1967 10 10 INTRA
## 1968 10 10 INTRA
## 1969 1 1 INTRA
## 1970 1 1 INTRA
## 1971 1 1 INTRA
## 1972 1 1 INTRA
## 1973 2 2 INTRA
## 1974 2 2 INTRA
## 1975 2 2 INTRA
## 1976 2 2 INTRA
## 1977 4 4 INTRA
## 1978 4 4 INTRA
## 1979 4 4 INTRA
## 1980 4 4 INTRA
## 1981 10 10 INTRA
## 1982 10 10 INTRA
## 1983 10 10 INTRA
## 1984 10 10 INTRA
## 1985 1 1 INTRA
## 1986 1 1 INTRA
## 1987 1 1 INTRA
## 1988 1 1 INTRA
## 1989 2 2 INTRA
## 1990 2 2 INTRA
## 1991 2 2 INTRA
## 1992 2 2 INTRA
## 1993 4 4 INTRA
## 1994 4 4 INTRA
## 1995 4 4 INTRA
## 1996 4 4 INTRA
## 1997 10 10 INTRA
## 1998 10 10 INTRA
## 1999 10 10 INTRA
## 2000 10 10 INTRA
## 2001 1 1 INTRA
## 2002 1 1 INTRA
## 2003 1 1 INTRA
## 2004 1 1 INTRA
## 2005 2 2 INTRA
## 2006 2 2 INTRA
## 2007 2 2 INTRA
## 2008 2 2 INTRA
## 2009 4 4 INTRA
## 2010 4 4 INTRA
## 2011 4 4 INTRA
## 2012 4 4 INTRA
## 2013 10 10 INTRA
## 2014 10 10 INTRA
## 2015 10 10 INTRA
## 2016 10 10 INTRA
## 2017 1 1 INTRA
## 2018 1 1 INTRA
## 2019 1 1 INTRA
## 2020 1 1 INTRA
## 2021 2 2 INTRA
## 2022 2 2 INTRA
## 2023 2 2 INTRA
## 2024 2 2 INTRA
## 2025 4 4 INTRA
## 2026 4 4 INTRA
## 2027 4 4 INTRA
## 2028 4 4 INTRA
## 2029 10 10 INTRA
## 2030 10 10 INTRA
## 2031 10 10 INTRA
## 2032 10 10 INTRA
## 2033 1 1 INTRA
## 2034 1 1 INTRA
## 2035 1 1 INTRA
## 2036 1 1 INTRA
## 2037 2 2 INTRA
## 2038 2 2 INTRA
## 2039 2 2 INTRA
## 2040 2 2 INTRA
## 2041 4 4 INTRA
## 2042 4 4 INTRA
## 2043 4 4 INTRA
## 2044 4 4 INTRA
## 2045 10 10 INTRA
## 2046 10 10 INTRA
## 2047 10 10 INTRA
## 2048 10 10 INTRA
## 2049 1 1 INTRA
## 2050 1 1 INTRA
## 2051 1 1 INTRA
## 2052 1 1 INTRA
## 2053 2 2 INTRA
## 2054 2 2 INTRA
## 2055 2 2 INTRA
## 2056 2 2 INTRA
## 2057 4 4 INTRA
## 2058 4 4 INTRA
## 2059 4 4 INTRA
## 2060 4 4 INTRA
## 2061 10 10 INTRA
## 2062 10 10 INTRA
## 2063 10 10 INTRA
## 2064 10 10 INTRA
## 2065 1 1 INTRA
## 2066 1 1 INTRA
## 2067 1 1 INTRA
## 2068 1 1 INTRA
## 2069 2 2 INTRA
## 2070 2 2 INTRA
## 2071 2 2 INTRA
## 2072 2 2 INTRA
## 2073 4 4 INTRA
## 2074 4 4 INTRA
## 2075 4 4 INTRA
## 2076 4 4 INTRA
## 2077 10 10 INTRA
## 2078 10 10 INTRA
## 2079 10 10 INTRA
## 2080 10 10 INTRA
## 2081 1 1 INTRA
## 2082 1 1 INTRA
## 2083 1 1 INTRA
## 2084 1 1 INTRA
## 2085 2 2 INTRA
## 2086 2 2 INTRA
## 2087 2 2 INTRA
## 2088 2 2 INTRA
## 2089 4 4 INTRA
## 2090 4 4 INTRA
## 2091 4 4 INTRA
## 2092 4 4 INTRA
## 2093 10 10 INTRA
## 2094 10 10 INTRA
## 2095 10 10 INTRA
## 2096 10 10 INTRA
## 2097 1 2 INTER
## 2098 1 2 INTER
## 2099 1 2 INTER
## 2100 1 2 INTER
## 2101 1 4 INTER
## 2102 1 4 INTER
## 2103 1 4 INTER
## 2104 1 4 INTER
## 2105 1 10 INTER
## 2106 1 10 INTER
## 2107 1 10 INTER
## 2108 1 10 INTER
## 2109 1 2 INTER
## 2110 1 2 INTER
## 2111 1 2 INTER
## 2112 1 2 INTER
## 2113 1 4 INTER
## 2114 1 4 INTER
## 2115 1 4 INTER
## 2116 1 4 INTER
## 2117 1 10 INTER
## 2118 1 10 INTER
## 2119 1 10 INTER
## 2120 1 10 INTER
## 2121 1 2 INTER
## 2122 1 2 INTER
## 2123 1 2 INTER
## 2124 1 2 INTER
## 2125 1 4 INTER
## 2126 1 4 INTER
## 2127 1 4 INTER
## 2128 1 4 INTER
## 2129 1 10 INTER
## 2130 1 10 INTER
## 2131 1 10 INTER
## 2132 1 10 INTER
## 2133 1 2 INTER
## 2134 1 2 INTER
## 2135 1 2 INTER
## 2136 1 2 INTER
## 2137 1 4 INTER
## 2138 1 4 INTER
## 2139 1 4 INTER
## 2140 1 4 INTER
## 2141 1 10 INTER
## 2142 1 10 INTER
## 2143 1 10 INTER
## 2144 1 10 INTER
## 2145 1 2 INTER
## 2146 1 2 INTER
## 2147 1 2 INTER
## 2148 1 2 INTER
## 2149 1 4 INTER
## 2150 1 4 INTER
## 2151 1 4 INTER
## 2152 1 4 INTER
## 2153 1 10 INTER
## 2154 1 10 INTER
## 2155 1 10 INTER
## 2156 1 10 INTER
## 2157 1 2 INTER
## 2158 1 2 INTER
## 2159 1 2 INTER
## 2160 1 2 INTER
## 2161 1 4 INTER
## 2162 1 4 INTER
## 2163 1 4 INTER
## 2164 1 4 INTER
## 2165 1 10 INTER
## 2166 1 10 INTER
## 2167 1 10 INTER
## 2168 1 10 INTER
## 2169 1 2 INTER
## 2170 1 2 INTER
## 2171 1 2 INTER
## 2172 1 2 INTER
## 2173 1 4 INTER
## 2174 1 4 INTER
## 2175 1 4 INTER
## 2176 1 4 INTER
## 2177 1 10 INTER
## 2178 1 10 INTER
## 2179 1 10 INTER
## 2180 1 10 INTER
## 2181 1 2 INTER
## 2182 1 2 INTER
## 2183 1 2 INTER
## 2184 1 2 INTER
## 2185 1 4 INTER
## 2186 1 4 INTER
## 2187 1 4 INTER
## 2188 1 4 INTER
## 2189 1 10 INTER
## 2190 1 10 INTER
## 2191 1 10 INTER
## 2192 1 10 INTER
## 2193 1 2 INTER
## 2194 1 2 INTER
## 2195 1 2 INTER
## 2196 1 2 INTER
## 2197 1 4 INTER
## 2198 1 4 INTER
## 2199 1 4 INTER
## 2200 1 4 INTER
## 2201 1 10 INTER
## 2202 1 10 INTER
## 2203 1 10 INTER
## 2204 1 10 INTER
## 2205 1 2 INTER
## 2206 1 2 INTER
## 2207 1 2 INTER
## 2208 1 2 INTER
## 2209 1 4 INTER
## 2210 1 4 INTER
## 2211 1 4 INTER
## 2212 1 4 INTER
## 2213 1 10 INTER
## 2214 1 10 INTER
## 2215 1 10 INTER
## 2216 1 10 INTER
## 2217 1 2 INTER
## 2218 1 2 INTER
## 2219 1 2 INTER
## 2220 1 2 INTER
## 2221 1 4 INTER
## 2222 1 4 INTER
## 2223 1 4 INTER
## 2224 1 4 INTER
## 2225 1 10 INTER
## 2226 1 10 INTER
## 2227 1 10 INTER
## 2228 1 10 INTER
## 2229 1 2 INTER
## 2230 1 2 INTER
## 2231 1 2 INTER
## 2232 1 2 INTER
## 2233 1 4 INTER
## 2234 1 4 INTER
## 2235 1 4 INTER
## 2236 1 4 INTER
## 2237 1 10 INTER
## 2238 1 10 INTER
## 2239 1 10 INTER
## 2240 1 10 INTER
## 2241 1 1 INTRA
## 2242 1 1 INTRA
## 2243 1 1 INTRA
## 2244 1 1 INTRA
## 2245 2 2 INTRA
## 2246 2 2 INTRA
## 2247 2 2 INTRA
## 2248 2 2 INTRA
## 2249 4 4 INTRA
## 2250 4 4 INTRA
## 2251 4 4 INTRA
## 2252 4 4 INTRA
## 2253 10 10 INTRA
## 2254 10 10 INTRA
## 2255 10 10 INTRA
## 2256 10 10 INTRA
## 2257 1 1 INTRA
## 2258 1 1 INTRA
## 2259 1 1 INTRA
## 2260 1 1 INTRA
## 2261 2 2 INTRA
## 2262 2 2 INTRA
## 2263 2 2 INTRA
## 2264 2 2 INTRA
## 2265 4 4 INTRA
## 2266 4 4 INTRA
## 2267 4 4 INTRA
## 2268 4 4 INTRA
## 2269 10 10 INTRA
## 2270 10 10 INTRA
## 2271 10 10 INTRA
## 2272 10 10 INTRA
## 2273 1 1 INTRA
## 2274 1 1 INTRA
## 2275 1 1 INTRA
## 2276 1 1 INTRA
## 2277 2 2 INTRA
## 2278 2 2 INTRA
## 2279 2 2 INTRA
## 2280 2 2 INTRA
## 2281 4 4 INTRA
## 2282 4 4 INTRA
## 2283 4 4 INTRA
## 2284 4 4 INTRA
## 2285 10 10 INTRA
## 2286 10 10 INTRA
## 2287 10 10 INTRA
## 2288 10 10 INTRA
## 2289 1 1 INTRA
## 2290 1 1 INTRA
## 2291 1 1 INTRA
## 2292 1 1 INTRA
## 2293 2 2 INTRA
## 2294 2 2 INTRA
## 2295 2 2 INTRA
## 2296 2 2 INTRA
## 2297 4 4 INTRA
## 2298 4 4 INTRA
## 2299 4 4 INTRA
## 2300 4 4 INTRA
## 2301 10 10 INTRA
## 2302 10 10 INTRA
## 2303 10 10 INTRA
## 2304 10 10 INTRA
## 2305 1 1 INTRA
## 2306 1 1 INTRA
## 2307 1 1 INTRA
## 2308 1 1 INTRA
## 2309 2 2 INTRA
## 2310 2 2 INTRA
## 2311 2 2 INTRA
## 2312 2 2 INTRA
## 2313 4 4 INTRA
## 2314 4 4 INTRA
## 2315 4 4 INTRA
## 2316 4 4 INTRA
## 2317 10 10 INTRA
## 2318 10 10 INTRA
## 2319 10 10 INTRA
## 2320 10 10 INTRA
## 2321 1 1 INTRA
## 2322 1 1 INTRA
## 2323 1 1 INTRA
## 2324 1 1 INTRA
## 2325 2 2 INTRA
## 2326 2 2 INTRA
## 2327 2 2 INTRA
## 2328 2 2 INTRA
## 2329 4 4 INTRA
## 2330 4 4 INTRA
## 2331 4 4 INTRA
## 2332 4 4 INTRA
## 2333 10 10 INTRA
## 2334 10 10 INTRA
## 2335 10 10 INTRA
## 2336 10 10 INTRA
## 2337 1 2 INTER
## 2338 1 2 INTER
## 2339 1 2 INTER
## 2340 1 2 INTER
## 2341 1 4 INTER
## 2342 1 4 INTER
## 2343 1 4 INTER
## 2344 1 4 INTER
## 2345 1 10 INTER
## 2346 1 10 INTER
## 2347 1 10 INTER
## 2348 1 10 INTER
## 2349 1 2 INTER
## 2350 1 2 INTER
## 2351 1 2 INTER
## 2352 1 2 INTER
## 2353 1 4 INTER
## 2354 1 4 INTER
## 2355 1 4 INTER
## 2356 1 4 INTER
## 2357 1 10 INTER
## 2358 1 10 INTER
## 2359 1 10 INTER
## 2360 1 10 INTER
## 2361 1 2 INTER
## 2362 1 2 INTER
## 2363 1 2 INTER
## 2364 1 2 INTER
## 2365 1 4 INTER
## 2366 1 4 INTER
## 2367 1 4 INTER
## 2368 1 4 INTER
## 2369 1 10 INTER
## 2370 1 10 INTER
## 2371 1 10 INTER
## 2372 1 10 INTER
## 2373 1 2 INTER
## 2374 1 2 INTER
## 2375 1 2 INTER
## 2376 1 2 INTER
## 2377 1 4 INTER
## 2378 1 4 INTER
## 2379 1 4 INTER
## 2380 1 4 INTER
## 2381 1 10 INTER
## 2382 1 10 INTER
## 2383 1 10 INTER
## 2384 1 10 INTER
## 2385 1 2 INTER
## 2386 1 2 INTER
## 2387 1 2 INTER
## 2388 1 2 INTER
## 2389 1 4 INTER
## 2390 1 4 INTER
## 2391 1 4 INTER
## 2392 1 4 INTER
## 2393 1 10 INTER
## 2394 1 10 INTER
## 2395 1 10 INTER
## 2396 1 10 INTER
## 2397 1 2 INTER
## 2398 1 2 INTER
## 2399 1 2 INTER
## 2400 1 2 INTER
## 2401 1 4 INTER
## 2402 1 4 INTER
## 2403 1 4 INTER
## 2404 1 4 INTER
## 2405 1 10 INTER
## 2406 1 10 INTER
## 2407 1 10 INTER
## 2408 1 10 INTER
## 2409 1 2 INTER
## 2410 1 2 INTER
## 2411 1 2 INTER
## 2412 1 2 INTER
## 2413 1 4 INTER
## 2414 1 4 INTER
## 2415 1 4 INTER
## 2416 1 4 INTER
## 2417 1 10 INTER
## 2418 1 10 INTER
## 2419 1 10 INTER
## 2420 1 10 INTER
## 2421 1 2 INTER
## 2422 1 2 INTER
## 2423 1 2 INTER
## 2424 1 2 INTER
## 2425 1 4 INTER
## 2426 1 4 INTER
## 2427 1 4 INTER
## 2428 1 4 INTER
## 2429 1 10 INTER
## 2430 1 10 INTER
## 2431 1 10 INTER
## 2432 1 10 INTER
## 2433 1 2 INTER
## 2434 1 2 INTER
## 2435 1 2 INTER
## 2436 1 2 INTER
## 2437 1 4 INTER
## 2438 1 4 INTER
## 2439 1 4 INTER
## 2440 1 4 INTER
## 2441 1 10 INTER
## 2442 1 10 INTER
## 2443 1 10 INTER
## 2444 1 10 INTER
## 2445 1 2 INTER
## 2446 1 2 INTER
## 2447 1 2 INTER
## 2448 1 2 INTER
## 2449 1 4 INTER
## 2450 1 4 INTER
## 2451 1 4 INTER
## 2452 1 4 INTER
## 2453 1 10 INTER
## 2454 1 10 INTER
## 2455 1 10 INTER
## 2456 1 10 INTER
## 2457 1 2 INTER
## 2458 1 2 INTER
## 2459 1 2 INTER
## 2460 1 2 INTER
## 2461 1 4 INTER
## 2462 1 4 INTER
## 2463 1 4 INTER
## 2464 1 4 INTER
## 2465 1 10 INTER
## 2466 1 10 INTER
## 2467 1 10 INTER
## 2468 1 10 INTER
## 2469 1 2 INTER
## 2470 1 2 INTER
## 2471 1 2 INTER
## 2472 1 2 INTER
## 2473 1 4 INTER
## 2474 1 4 INTER
## 2475 1 4 INTER
## 2476 1 4 INTER
## 2477 1 10 INTER
## 2478 1 10 INTER
## 2479 1 10 INTER
## 2480 1 10 INTER
## 2481 1 1 INTRA
## 2482 1 1 INTRA
## 2483 1 1 INTRA
## 2484 1 1 INTRA
## 2485 2 2 INTRA
## 2486 2 2 INTRA
## 2487 2 2 INTRA
## 2488 2 2 INTRA
## 2489 4 4 INTRA
## 2490 4 4 INTRA
## 2491 4 4 INTRA
## 2492 4 4 INTRA
## 2493 10 10 INTRA
## 2494 10 10 INTRA
## 2495 10 10 INTRA
## 2496 10 10 INTRA
## 2497 1 1 INTRA
## 2498 1 1 INTRA
## 2499 1 1 INTRA
## 2500 1 1 INTRA
## 2501 2 2 INTRA
## 2502 2 2 INTRA
## 2503 2 2 INTRA
## 2504 2 2 INTRA
## 2505 4 4 INTRA
## 2506 4 4 INTRA
## 2507 4 4 INTRA
## 2508 4 4 INTRA
## 2509 10 10 INTRA
## 2510 10 10 INTRA
## 2511 10 10 INTRA
## 2512 10 10 INTRA
## 2513 1 1 INTRA
## 2514 1 1 INTRA
## 2515 1 1 INTRA
## 2516 1 1 INTRA
## 2517 2 2 INTRA
## 2518 2 2 INTRA
## 2519 2 2 INTRA
## 2520 2 2 INTRA
## 2521 4 4 INTRA
## 2522 4 4 INTRA
## 2523 4 4 INTRA
## 2524 4 4 INTRA
## 2525 10 10 INTRA
## 2526 10 10 INTRA
## 2527 10 10 INTRA
## 2528 10 10 INTRA
## 2529 1 2 INTER
## 2530 1 2 INTER
## 2531 1 2 INTER
## 2532 1 2 INTER
## 2533 1 4 INTER
## 2534 1 4 INTER
## 2535 1 4 INTER
## 2536 1 4 INTER
## 2537 1 10 INTER
## 2538 1 10 INTER
## 2539 1 10 INTER
## 2540 1 10 INTER
## 2541 1 2 INTER
## 2542 1 2 INTER
## 2543 1 2 INTER
## 2544 1 2 INTER
## 2545 1 4 INTER
## 2546 1 4 INTER
## 2547 1 4 INTER
## 2548 1 4 INTER
## 2549 1 10 INTER
## 2550 1 10 INTER
## 2551 1 10 INTER
## 2552 1 10 INTER
## 2553 1 2 INTER
## 2554 1 2 INTER
## 2555 1 2 INTER
## 2556 1 2 INTER
## 2557 1 4 INTER
## 2558 1 4 INTER
## 2559 1 4 INTER
## 2560 1 4 INTER
## 2561 1 10 INTER
## 2562 1 10 INTER
## 2563 1 10 INTER
## 2564 1 10 INTER
## 2565 1 2 INTER
## 2566 1 2 INTER
## 2567 1 2 INTER
## 2568 1 2 INTER
## 2569 1 4 INTER
## 2570 1 4 INTER
## 2571 1 4 INTER
## 2572 1 4 INTER
## 2573 1 10 INTER
## 2574 1 10 INTER
## 2575 1 10 INTER
## 2576 1 10 INTER
## 2577 1 2 INTER
## 2578 1 2 INTER
## 2579 1 2 INTER
## 2580 1 2 INTER
## 2581 1 4 INTER
## 2582 1 4 INTER
## 2583 1 4 INTER
## 2584 1 4 INTER
## 2585 1 10 INTER
## 2586 1 10 INTER
## 2587 1 10 INTER
## 2588 1 10 INTER
## 2589 1 2 INTER
## 2590 1 2 INTER
## 2591 1 2 INTER
## 2592 1 2 INTER
## 2593 1 4 INTER
## 2594 1 4 INTER
## 2595 1 4 INTER
## 2596 1 4 INTER
## 2597 1 10 INTER
## 2598 1 10 INTER
## 2599 1 10 INTER
## 2600 1 10 INTER
## 2601 1 1 INTRA
## 2602 1 1 INTRA
## 2603 1 1 INTRA
## 2604 1 1 INTRA
## 2605 2 2 INTRA
## 2606 2 2 INTRA
## 2607 2 2 INTRA
## 2608 2 2 INTRA
## 2609 4 4 INTRA
## 2610 4 4 INTRA
## 2611 4 4 INTRA
## 2612 4 4 INTRA
## 2613 10 10 INTRA
## 2614 10 10 INTRA
## 2615 10 10 INTRA
## 2616 10 10 INTRA
## 2617 1 1 INTRA
## 2618 1 1 INTRA
## 2619 1 1 INTRA
## 2620 1 1 INTRA
## 2621 2 2 INTRA
## 2622 2 2 INTRA
## 2623 2 2 INTRA
## 2624 2 2 INTRA
## 2625 4 4 INTRA
## 2626 4 4 INTRA
## 2627 4 4 INTRA
## 2628 4 4 INTRA
## 2629 10 10 INTRA
## 2630 10 10 INTRA
## 2631 10 10 INTRA
## 2632 10 10 INTRA
## 2633 1 1 INTRA
## 2634 1 1 INTRA
## 2635 1 1 INTRA
## 2636 1 1 INTRA
## 2637 2 2 INTRA
## 2638 2 2 INTRA
## 2639 2 2 INTRA
## 2640 2 2 INTRA
## 2641 4 4 INTRA
## 2642 4 4 INTRA
## 2643 4 4 INTRA
## 2644 4 4 INTRA
## 2645 10 10 INTRA
## 2646 10 10 INTRA
## 2647 10 10 INTRA
## 2648 10 10 INTRA
## 2649 1 2 INTER
## 2650 1 2 INTER
## 2651 1 2 INTER
## 2652 1 2 INTER
## 2653 1 4 INTER
## 2654 1 4 INTER
## 2655 1 4 INTER
## 2656 1 4 INTER
## 2657 1 10 INTER
## 2658 1 10 INTER
## 2659 1 10 INTER
## 2660 1 10 INTER
## 2661 1 2 INTER
## 2662 1 2 INTER
## 2663 1 2 INTER
## 2664 1 2 INTER
## 2665 1 4 INTER
## 2666 1 4 INTER
## 2667 1 4 INTER
## 2668 1 4 INTER
## 2669 1 10 INTER
## 2670 1 10 INTER
## 2671 1 10 INTER
## 2672 1 10 INTER
## 2673 1 2 INTER
## 2674 1 2 INTER
## 2675 1 2 INTER
## 2676 1 2 INTER
## 2677 1 4 INTER
## 2678 1 4 INTER
## 2679 1 4 INTER
## 2680 1 4 INTER
## 2681 1 10 INTER
## 2682 1 10 INTER
## 2683 1 10 INTER
## 2684 1 10 INTER
## 2685 1 2 INTER
## 2686 1 2 INTER
## 2687 1 2 INTER
## 2688 1 2 INTER
## 2689 1 4 INTER
## 2690 1 4 INTER
## 2691 1 4 INTER
## 2692 1 4 INTER
## 2693 1 10 INTER
## 2694 1 10 INTER
## 2695 1 10 INTER
## 2696 1 10 INTER
## 2697 1 2 INTER
## 2698 1 2 INTER
## 2699 1 2 INTER
## 2700 1 2 INTER
## 2701 1 4 INTER
## 2702 1 4 INTER
## 2703 1 4 INTER
## 2704 1 4 INTER
## 2705 1 10 INTER
## 2706 1 10 INTER
## 2707 1 10 INTER
## 2708 1 10 INTER
## 2709 1 2 INTER
## 2710 1 2 INTER
## 2711 1 2 INTER
## 2712 1 2 INTER
## 2713 1 4 INTER
## 2714 1 4 INTER
## 2715 1 4 INTER
## 2716 1 4 INTER
## 2717 1 10 INTER
## 2718 1 10 INTER
## 2719 1 10 INTER
## 2720 1 10 INTER
## 2721 1 1 INTRA
## 2722 1 1 INTRA
## 2723 1 1 INTRA
## 2724 1 1 INTRA
## 2725 2 2 INTRA
## 2726 2 2 INTRA
## 2727 2 2 INTRA
## 2728 2 2 INTRA
## 2729 4 4 INTRA
## 2730 4 4 INTRA
## 2731 4 4 INTRA
## 2732 4 4 INTRA
## 2733 10 10 INTRA
## 2734 10 10 INTRA
## 2735 10 10 INTRA
## 2736 10 10 INTRA
## 2737 1 1 INTRA
## 2738 1 1 INTRA
## 2739 1 1 INTRA
## 2740 1 1 INTRA
## 2741 2 2 INTRA
## 2742 2 2 INTRA
## 2743 2 2 INTRA
## 2744 2 2 INTRA
## 2745 4 4 INTRA
## 2746 4 4 INTRA
## 2747 4 4 INTRA
## 2748 4 4 INTRA
## 2749 10 10 INTRA
## 2750 10 10 INTRA
## 2751 10 10 INTRA
## 2752 10 10 INTRA
## 2753 1 1 INTRA
## 2754 1 1 INTRA
## 2755 1 1 INTRA
## 2756 1 1 INTRA
## 2757 2 2 INTRA
## 2758 2 2 INTRA
## 2759 2 2 INTRA
## 2760 2 2 INTRA
## 2761 4 4 INTRA
## 2762 4 4 INTRA
## 2763 4 4 INTRA
## 2764 4 4 INTRA
## 2765 10 10 INTRA
## 2766 10 10 INTRA
## 2767 10 10 INTRA
## 2768 10 10 INTRA
## 2769 1 1 INTRA
## 2770 1 1 INTRA
## 2771 1 1 INTRA
## 2772 1 1 INTRA
## 2773 2 2 INTRA
## 2774 2 2 INTRA
## 2775 2 2 INTRA
## 2776 2 2 INTRA
## 2777 4 4 INTRA
## 2778 4 4 INTRA
## 2779 4 4 INTRA
## 2780 4 4 INTRA
## 2781 10 10 INTRA
## 2782 10 10 INTRA
## 2783 10 10 INTRA
## 2784 10 10 INTRA
## 2785 1 1 INTRA
## 2786 1 1 INTRA
## 2787 1 1 INTRA
## 2788 1 1 INTRA
## 2789 2 2 INTRA
## 2790 2 2 INTRA
## 2791 2 2 INTRA
## 2792 2 2 INTRA
## 2793 4 4 INTRA
## 2794 4 4 INTRA
## 2795 4 4 INTRA
## 2796 4 4 INTRA
## 2797 10 10 INTRA
## 2798 10 10 INTRA
## 2799 10 10 INTRA
## 2800 10 10 INTRA
## 2801 1 1 INTRA
## 2802 1 1 INTRA
## 2803 1 1 INTRA
## 2804 1 1 INTRA
## 2805 2 2 INTRA
## 2806 2 2 INTRA
## 2807 2 2 INTRA
## 2808 2 2 INTRA
## 2809 4 4 INTRA
## 2810 4 4 INTRA
## 2811 4 4 INTRA
## 2812 4 4 INTRA
## 2813 10 10 INTRA
## 2814 10 10 INTRA
## 2815 10 10 INTRA
## 2816 10 10 INTRA
## 2817 1 2 INTER
## 2818 1 2 INTER
## 2819 1 2 INTER
## 2820 1 2 INTER
## 2821 1 4 INTER
## 2822 1 4 INTER
## 2823 1 4 INTER
## 2824 1 4 INTER
## 2825 1 10 INTER
## 2826 1 10 INTER
## 2827 1 10 INTER
## 2828 1 10 INTER
## 2829 1 2 INTER
## 2830 1 2 INTER
## 2831 1 2 INTER
## 2832 1 2 INTER
## 2833 1 4 INTER
## 2834 1 4 INTER
## 2835 1 4 INTER
## 2836 1 4 INTER
## 2837 1 10 INTER
## 2838 1 10 INTER
## 2839 1 10 INTER
## 2840 1 10 INTER
## 2841 1 2 INTER
## 2842 1 2 INTER
## 2843 1 2 INTER
## 2844 1 2 INTER
## 2845 1 4 INTER
## 2846 1 4 INTER
## 2847 1 4 INTER
## 2848 1 4 INTER
## 2849 1 10 INTER
## 2850 1 10 INTER
## 2851 1 10 INTER
## 2852 1 10 INTER
## 2853 1 2 INTER
## 2854 1 2 INTER
## 2855 1 2 INTER
## 2856 1 2 INTER
## 2857 1 4 INTER
## 2858 1 4 INTER
## 2859 1 4 INTER
## 2860 1 4 INTER
## 2861 1 10 INTER
## 2862 1 10 INTER
## 2863 1 10 INTER
## 2864 1 10 INTER
## 2865 1 2 INTER
## 2866 1 2 INTER
## 2867 1 2 INTER
## 2868 1 2 INTER
## 2869 1 4 INTER
## 2870 1 4 INTER
## 2871 1 4 INTER
## 2872 1 4 INTER
## 2873 1 10 INTER
## 2874 1 10 INTER
## 2875 1 10 INTER
## 2876 1 10 INTER
## 2877 1 2 INTER
## 2878 1 2 INTER
## 2879 1 2 INTER
## 2880 1 2 INTER
## 2881 1 4 INTER
## 2882 1 4 INTER
## 2883 1 4 INTER
## 2884 1 4 INTER
## 2885 1 10 INTER
## 2886 1 10 INTER
## 2887 1 10 INTER
## 2888 1 10 INTER
## 2889 1 2 INTER
## 2890 1 2 INTER
## 2891 1 2 INTER
## 2892 1 2 INTER
## 2893 1 4 INTER
## 2894 1 4 INTER
## 2895 1 4 INTER
## 2896 1 4 INTER
## 2897 1 10 INTER
## 2898 1 10 INTER
## 2899 1 10 INTER
## 2900 1 10 INTER
## 2901 1 2 INTER
## 2902 1 2 INTER
## 2903 1 2 INTER
## 2904 1 2 INTER
## 2905 1 4 INTER
## 2906 1 4 INTER
## 2907 1 4 INTER
## 2908 1 4 INTER
## 2909 1 10 INTER
## 2910 1 10 INTER
## 2911 1 10 INTER
## 2912 1 10 INTER
## 2913 1 2 INTER
## 2914 1 2 INTER
## 2915 1 2 INTER
## 2916 1 2 INTER
## 2917 1 4 INTER
## 2918 1 4 INTER
## 2919 1 4 INTER
## 2920 1 4 INTER
## 2921 1 10 INTER
## 2922 1 10 INTER
## 2923 1 10 INTER
## 2924 1 10 INTER
## 2925 1 2 INTER
## 2926 1 2 INTER
## 2927 1 2 INTER
## 2928 1 2 INTER
## 2929 1 4 INTER
## 2930 1 4 INTER
## 2931 1 4 INTER
## 2932 1 4 INTER
## 2933 1 10 INTER
## 2934 1 10 INTER
## 2935 1 10 INTER
## 2936 1 10 INTER
## 2937 1 2 INTER
## 2938 1 2 INTER
## 2939 1 2 INTER
## 2940 1 2 INTER
## 2941 1 4 INTER
## 2942 1 4 INTER
## 2943 1 4 INTER
## 2944 1 4 INTER
## 2945 1 10 INTER
## 2946 1 10 INTER
## 2947 1 10 INTER
## 2948 1 10 INTER
## 2949 1 2 INTER
## 2950 1 2 INTER
## 2951 1 2 INTER
## 2952 1 2 INTER
## 2953 1 4 INTER
## 2954 1 4 INTER
## 2955 1 4 INTER
## 2956 1 4 INTER
## 2957 1 10 INTER
## 2958 1 10 INTER
## 2959 1 10 INTER
## 2960 1 10 INTER
## 2961 1 1 INTRA
## 2962 1 1 INTRA
## 2963 1 1 INTRA
## 2964 1 1 INTRA
## 2965 2 2 INTRA
## 2966 2 2 INTRA
## 2967 2 2 INTRA
## 2968 2 2 INTRA
## 2969 4 4 INTRA
## 2970 4 4 INTRA
## 2971 4 4 INTRA
## 2972 4 4 INTRA
## 2973 10 10 INTRA
## 2974 10 10 INTRA
## 2975 10 10 INTRA
## 2976 10 10 INTRA
## 2977 1 1 INTRA
## 2978 1 1 INTRA
## 2979 1 1 INTRA
## 2980 1 1 INTRA
## 2981 2 2 INTRA
## 2982 2 2 INTRA
## 2983 2 2 INTRA
## 2984 2 2 INTRA
## 2985 4 4 INTRA
## 2986 4 4 INTRA
## 2987 4 4 INTRA
## 2988 4 4 INTRA
## 2989 10 10 INTRA
## 2990 10 10 INTRA
## 2991 10 10 INTRA
## 2992 10 10 INTRA
## 2993 1 1 INTRA
## 2994 1 1 INTRA
## 2995 1 1 INTRA
## 2996 1 1 INTRA
## 2997 2 2 INTRA
## 2998 2 2 INTRA
## 2999 2 2 INTRA
## 3000 2 2 INTRA
## 3001 4 4 INTRA
## 3002 4 4 INTRA
## 3003 4 4 INTRA
## 3004 4 4 INTRA
## 3005 10 10 INTRA
## 3006 10 10 INTRA
## 3007 10 10 INTRA
## 3008 10 10 INTRA
## 3009 1 2 INTER
## 3010 1 2 INTER
## 3011 1 2 INTER
## 3012 1 2 INTER
## 3013 1 4 INTER
## 3014 1 4 INTER
## 3015 1 4 INTER
## 3016 1 4 INTER
## 3017 1 10 INTER
## 3018 1 10 INTER
## 3019 1 10 INTER
## 3020 1 10 INTER
## 3021 1 2 INTER
## 3022 1 2 INTER
## 3023 1 2 INTER
## 3024 1 2 INTER
## 3025 1 4 INTER
## 3026 1 4 INTER
## 3027 1 4 INTER
## 3028 1 4 INTER
## 3029 1 10 INTER
## 3030 1 10 INTER
## 3031 1 10 INTER
## 3032 1 10 INTER
## 3033 1 2 INTER
## 3034 1 2 INTER
## 3035 1 2 INTER
## 3036 1 2 INTER
## 3037 1 4 INTER
## 3038 1 4 INTER
## 3039 1 4 INTER
## 3040 1 4 INTER
## 3041 1 10 INTER
## 3042 1 10 INTER
## 3043 1 10 INTER
## 3044 1 10 INTER
## 3045 1 1 INTRA
## 3046 1 1 INTRA
## 3047 1 1 INTRA
## 3048 1 1 INTRA
## 3049 2 2 INTRA
## 3050 2 2 INTRA
## 3051 2 2 INTRA
## 3052 2 2 INTRA
## 3053 4 4 INTRA
## 3054 4 4 INTRA
## 3055 4 4 INTRA
## 3056 4 4 INTRA
## 3057 10 10 INTRA
## 3058 10 10 INTRA
## 3059 10 10 INTRA
## 3060 10 10 INTRA
## 3061 1 1 INTRA
## 3062 1 1 INTRA
## 3063 1 1 INTRA
## 3064 1 1 INTRA
## 3065 2 2 INTRA
## 3066 2 2 INTRA
## 3067 2 2 INTRA
## 3068 2 2 INTRA
## 3069 4 4 INTRA
## 3070 4 4 INTRA
## 3071 4 4 INTRA
## 3072 4 4 INTRA
## 3073 10 10 INTRA
## 3074 10 10 INTRA
## 3075 10 10 INTRA
## 3076 10 10 INTRA
## 3077 1 1 INTRA
## 3078 1 1 INTRA
## 3079 1 1 INTRA
## 3080 1 1 INTRA
## 3081 2 2 INTRA
## 3082 2 2 INTRA
## 3083 2 2 INTRA
## 3084 2 2 INTRA
## 3085 4 4 INTRA
## 3086 4 4 INTRA
## 3087 4 4 INTRA
## 3088 4 4 INTRA
## 3089 10 10 INTRA
## 3090 10 10 INTRA
## 3091 10 10 INTRA
## 3092 10 10 INTRA
## 3093 1 2 INTER
## 3094 1 2 INTER
## 3095 1 2 INTER
## 3096 1 2 INTER
## 3097 1 4 INTER
## 3098 1 4 INTER
## 3099 1 4 INTER
## 3100 1 4 INTER
## 3101 1 10 INTER
## 3102 1 10 INTER
## 3103 1 10 INTER
## 3104 1 10 INTER
## 3105 1 2 INTER
## 3106 1 2 INTER
## 3107 1 2 INTER
## 3108 1 2 INTER
## 3109 1 4 INTER
## 3110 1 4 INTER
## 3111 1 4 INTER
## 3112 1 4 INTER
## 3113 1 10 INTER
## 3114 1 10 INTER
## 3115 1 10 INTER
## 3116 1 10 INTER
## 3117 1 2 INTER
## 3118 1 2 INTER
## 3119 1 2 INTER
## 3120 1 2 INTER
## 3121 1 4 INTER
## 3122 1 4 INTER
## 3123 1 4 INTER
## 3124 1 4 INTER
## 3125 1 10 INTER
## 3126 1 10 INTER
## 3127 1 10 INTER
## 3128 1 10 INTER
## 3129 1 2 INTER
## 3130 1 2 INTER
## 3131 1 2 INTER
## 3132 1 2 INTER
## 3133 1 4 INTER
## 3134 1 4 INTER
## 3135 1 4 INTER
## 3136 1 4 INTER
## 3137 1 10 INTER
## 3138 1 10 INTER
## 3139 1 10 INTER
## 3140 1 10 INTER
## 3141 1 2 INTER
## 3142 1 2 INTER
## 3143 1 2 INTER
## 3144 1 2 INTER
## 3145 1 4 INTER
## 3146 1 4 INTER
## 3147 1 4 INTER
## 3148 1 4 INTER
## 3149 1 10 INTER
## 3150 1 10 INTER
## 3151 1 10 INTER
## 3152 1 10 INTER
## 3153 1 2 INTER
## 3154 1 2 INTER
## 3155 1 2 INTER
## 3156 1 2 INTER
## 3157 1 4 INTER
## 3158 1 4 INTER
## 3159 1 4 INTER
## 3160 1 4 INTER
## 3161 1 10 INTER
## 3162 1 10 INTER
## 3163 1 10 INTER
## 3164 1 10 INTER
## 3165 1 2 INTER
## 3166 1 2 INTER
## 3167 1 2 INTER
## 3168 1 2 INTER
## 3169 1 4 INTER
## 3170 1 4 INTER
## 3171 1 4 INTER
## 3172 1 4 INTER
## 3173 1 10 INTER
## 3174 1 10 INTER
## 3175 1 10 INTER
## 3176 1 10 INTER
## 3177 1 2 INTER
## 3178 1 2 INTER
## 3179 1 2 INTER
## 3180 1 2 INTER
## 3181 1 4 INTER
## 3182 1 4 INTER
## 3183 1 4 INTER
## 3184 1 4 INTER
## 3185 1 10 INTER
## 3186 1 10 INTER
## 3187 1 10 INTER
## 3188 1 10 INTER
## 3189 1 2 INTER
## 3190 1 2 INTER
## 3191 1 2 INTER
## 3192 1 2 INTER
## 3193 1 4 INTER
## 3194 1 4 INTER
## 3195 1 4 INTER
## 3196 1 4 INTER
## 3197 1 10 INTER
## 3198 1 10 INTER
## 3199 1 10 INTER
## 3200 1 10 INTER
## 3201 1 1 INTRA
## 3202 1 1 INTRA
## 3203 1 1 INTRA
## 3204 1 1 INTRA
## 3205 2 2 INTRA
## 3206 2 2 INTRA
## 3207 2 2 INTRA
## 3208 2 2 INTRA
## 3209 4 4 INTRA
## 3210 4 4 INTRA
## 3211 4 4 INTRA
## 3212 4 4 INTRA
## 3213 10 10 INTRA
## 3214 10 10 INTRA
## 3215 10 10 INTRA
## 3216 10 10 INTRA
## 3217 1 1 INTRA
## 3218 1 1 INTRA
## 3219 1 1 INTRA
## 3220 1 1 INTRA
## 3221 2 2 INTRA
## 3222 2 2 INTRA
## 3223 2 2 INTRA
## 3224 2 2 INTRA
## 3225 4 4 INTRA
## 3226 4 4 INTRA
## 3227 4 4 INTRA
## 3228 4 4 INTRA
## 3229 10 10 INTRA
## 3230 10 10 INTRA
## 3231 10 10 INTRA
## 3232 10 10 INTRA
## 3233 1 1 INTRA
## 3234 1 1 INTRA
## 3235 1 1 INTRA
## 3236 1 1 INTRA
## 3237 2 2 INTRA
## 3238 2 2 INTRA
## 3239 2 2 INTRA
## 3240 2 2 INTRA
## 3241 4 4 INTRA
## 3242 4 4 INTRA
## 3243 4 4 INTRA
## 3244 4 4 INTRA
## 3245 10 10 INTRA
## 3246 10 10 INTRA
## 3247 10 10 INTRA
## 3248 10 10 INTRA
## 3249 1 1 INTRA
## 3250 1 1 INTRA
## 3251 1 1 INTRA
## 3252 1 1 INTRA
## 3253 2 2 INTRA
## 3254 2 2 INTRA
## 3255 2 2 INTRA
## 3256 2 2 INTRA
## 3257 4 4 INTRA
## 3258 4 4 INTRA
## 3259 4 4 INTRA
## 3260 4 4 INTRA
## 3261 10 10 INTRA
## 3262 10 10 INTRA
## 3263 10 10 INTRA
## 3264 10 10 INTRA
## 3265 1 1 INTRA
## 3266 1 1 INTRA
## 3267 1 1 INTRA
## 3268 1 1 INTRA
## 3269 2 2 INTRA
## 3270 2 2 INTRA
## 3271 2 2 INTRA
## 3272 2 2 INTRA
## 3273 4 4 INTRA
## 3274 4 4 INTRA
## 3275 4 4 INTRA
## 3276 4 4 INTRA
## 3277 10 10 INTRA
## 3278 10 10 INTRA
## 3279 10 10 INTRA
## 3280 10 10 INTRA
## 3281 1 1 INTRA
## 3282 1 1 INTRA
## 3283 1 1 INTRA
## 3284 1 1 INTRA
## 3285 2 2 INTRA
## 3286 2 2 INTRA
## 3287 2 2 INTRA
## 3288 2 2 INTRA
## 3289 4 4 INTRA
## 3290 4 4 INTRA
## 3291 4 4 INTRA
## 3292 4 4 INTRA
## 3293 10 10 INTRA
## 3294 10 10 INTRA
## 3295 10 10 INTRA
## 3296 10 10 INTRA
## 3297 1 2 INTER
## 3298 1 2 INTER
## 3299 1 2 INTER
## 3300 1 2 INTER
## 3301 1 4 INTER
## 3302 1 4 INTER
## 3303 1 4 INTER
## 3304 1 4 INTER
## 3305 1 10 INTER
## 3306 1 10 INTER
## 3307 1 10 INTER
## 3308 1 10 INTER
## 3309 1 2 INTER
## 3310 1 2 INTER
## 3311 1 2 INTER
## 3312 1 2 INTER
## 3313 1 4 INTER
## 3314 1 4 INTER
## 3315 1 4 INTER
## 3316 1 4 INTER
## 3317 1 10 INTER
## 3318 1 10 INTER
## 3319 1 10 INTER
## 3320 1 10 INTER
## 3321 1 2 INTER
## 3322 1 2 INTER
## 3323 1 2 INTER
## 3324 1 2 INTER
## 3325 1 4 INTER
## 3326 1 4 INTER
## 3327 1 4 INTER
## 3328 1 4 INTER
## 3329 1 10 INTER
## 3330 1 10 INTER
## 3331 1 10 INTER
## 3332 1 10 INTER
## 3333 1 2 INTER
## 3334 1 2 INTER
## 3335 1 2 INTER
## 3336 1 2 INTER
## 3337 1 4 INTER
## 3338 1 4 INTER
## 3339 1 4 INTER
## 3340 1 4 INTER
## 3341 1 10 INTER
## 3342 1 10 INTER
## 3343 1 10 INTER
## 3344 1 10 INTER
## 3345 1 2 INTER
## 3346 1 2 INTER
## 3347 1 2 INTER
## 3348 1 2 INTER
## 3349 1 4 INTER
## 3350 1 4 INTER
## 3351 1 4 INTER
## 3352 1 4 INTER
## 3353 1 10 INTER
## 3354 1 10 INTER
## 3355 1 10 INTER
## 3356 1 10 INTER
## 3357 1 2 INTER
## 3358 1 2 INTER
## 3359 1 2 INTER
## 3360 1 2 INTER
## 3361 1 4 INTER
## 3362 1 4 INTER
## 3363 1 4 INTER
## 3364 1 4 INTER
## 3365 1 10 INTER
## 3366 1 10 INTER
## 3367 1 10 INTER
## 3368 1 10 INTER
## 3369 1 2 INTER
## 3370 1 2 INTER
## 3371 1 2 INTER
## 3372 1 2 INTER
## 3373 1 4 INTER
## 3374 1 4 INTER
## 3375 1 4 INTER
## 3376 1 4 INTER
## 3377 1 10 INTER
## 3378 1 10 INTER
## 3379 1 10 INTER
## 3380 1 10 INTER
## 3381 1 2 INTER
## 3382 1 2 INTER
## 3383 1 2 INTER
## 3384 1 2 INTER
## 3385 1 4 INTER
## 3386 1 4 INTER
## 3387 1 4 INTER
## 3388 1 4 INTER
## 3389 1 10 INTER
## 3390 1 10 INTER
## 3391 1 10 INTER
## 3392 1 10 INTER
## 3393 1 2 INTER
## 3394 1 2 INTER
## 3395 1 2 INTER
## 3396 1 2 INTER
## 3397 1 4 INTER
## 3398 1 4 INTER
## 3399 1 4 INTER
## 3400 1 4 INTER
## 3401 1 10 INTER
## 3402 1 10 INTER
## 3403 1 10 INTER
## 3404 1 10 INTER
## 3405 1 2 INTER
## 3406 1 2 INTER
## 3407 1 2 INTER
## 3408 1 2 INTER
## 3409 1 4 INTER
## 3410 1 4 INTER
## 3411 1 4 INTER
## 3412 1 4 INTER
## 3413 1 10 INTER
## 3414 1 10 INTER
## 3415 1 10 INTER
## 3416 1 10 INTER
## 3417 1 2 INTER
## 3418 1 2 INTER
## 3419 1 2 INTER
## 3420 1 2 INTER
## 3421 1 4 INTER
## 3422 1 4 INTER
## 3423 1 4 INTER
## 3424 1 4 INTER
## 3425 1 10 INTER
## 3426 1 10 INTER
## 3427 1 10 INTER
## 3428 1 10 INTER
## 3429 1 2 INTER
## 3430 1 2 INTER
## 3431 1 2 INTER
## 3432 1 2 INTER
## 3433 1 4 INTER
## 3434 1 4 INTER
## 3435 1 4 INTER
## 3436 1 4 INTER
## 3437 1 10 INTER
## 3438 1 10 INTER
## 3439 1 10 INTER
## 3440 1 10 INTER
## 3441 1 2 INTER
## 3442 1 2 INTER
## 3443 1 2 INTER
## 3444 1 2 INTER
## 3445 1 4 INTER
## 3446 1 4 INTER
## 3447 1 4 INTER
## 3448 1 4 INTER
## 3449 1 10 INTER
## 3450 1 10 INTER
## 3451 1 10 INTER
## 3452 1 10 INTER
## 3453 1 2 INTER
## 3454 1 2 INTER
## 3455 1 2 INTER
## 3456 1 2 INTER
## 3457 1 4 INTER
## 3458 1 4 INTER
## 3459 1 4 INTER
## 3460 1 4 INTER
## 3461 1 10 INTER
## 3462 1 10 INTER
## 3463 1 10 INTER
## 3464 1 10 INTER
## 3465 1 2 INTER
## 3466 1 2 INTER
## 3467 1 2 INTER
## 3468 1 2 INTER
## 3469 1 4 INTER
## 3470 1 4 INTER
## 3471 1 4 INTER
## 3472 1 4 INTER
## 3473 1 10 INTER
## 3474 1 10 INTER
## 3475 1 10 INTER
## 3476 1 10 INTER
## 3477 1 1 INTRA
## 3478 1 1 INTRA
## 3479 1 1 INTRA
## 3480 1 1 INTRA
## 3481 2 2 INTRA
## 3482 2 2 INTRA
## 3483 2 2 INTRA
## 3484 2 2 INTRA
## 3485 4 4 INTRA
## 3486 4 4 INTRA
## 3487 4 4 INTRA
## 3488 4 4 INTRA
## 3489 10 10 INTRA
## 3490 10 10 INTRA
## 3491 10 10 INTRA
## 3492 10 10 INTRA
## 3493 1 1 INTRA
## 3494 1 1 INTRA
## 3495 1 1 INTRA
## 3496 1 1 INTRA
## 3497 2 2 INTRA
## 3498 2 2 INTRA
## 3499 2 2 INTRA
## 3500 2 2 INTRA
## 3501 4 4 INTRA
## 3502 4 4 INTRA
## 3503 4 4 INTRA
## 3504 4 4 INTRA
## 3505 10 10 INTRA
## 3506 10 10 INTRA
## 3507 10 10 INTRA
## 3508 10 10 INTRA
## 3509 1 1 INTRA
## 3510 1 1 INTRA
## 3511 1 1 INTRA
## 3512 1 1 INTRA
## 3513 2 2 INTRA
## 3514 2 2 INTRA
## 3515 2 2 INTRA
## 3516 2 2 INTRA
## 3517 4 4 INTRA
## 3518 4 4 INTRA
## 3519 4 4 INTRA
## 3520 4 4 INTRA
## 3521 10 10 INTRA
## 3522 10 10 INTRA
## 3523 10 10 INTRA
## 3524 10 10 INTRA
## 3525 1 2 INTER
## 3526 1 2 INTER
## 3527 1 2 INTER
## 3528 1 2 INTER
## 3529 1 4 INTER
## 3530 1 4 INTER
## 3531 1 4 INTER
## 3532 1 4 INTER
## 3533 1 10 INTER
## 3534 1 10 INTER
## 3535 1 10 INTER
## 3536 1 10 INTER
## 3537 1 2 INTER
## 3538 1 2 INTER
## 3539 1 2 INTER
## 3540 1 2 INTER
## 3541 1 4 INTER
## 3542 1 4 INTER
## 3543 1 4 INTER
## 3544 1 4 INTER
## 3545 1 10 INTER
## 3546 1 10 INTER
## 3547 1 10 INTER
## 3548 1 10 INTER
## 3549 1 2 INTER
## 3550 1 2 INTER
## 3551 1 2 INTER
## 3552 1 2 INTER
## 3553 1 4 INTER
## 3554 1 4 INTER
## 3555 1 4 INTER
## 3556 1 4 INTER
## 3557 1 10 INTER
## 3558 1 10 INTER
## 3560 1 10 INTER
## 3561 1 2 INTER
## 3562 1 2 INTER
## 3563 1 2 INTER
## 3564 1 2 INTER
## 3565 1 4 INTER
## 3566 1 4 INTER
## 3567 1 4 INTER
## 3568 1 4 INTER
## 3569 1 10 INTER
## 3570 1 10 INTER
## 3571 1 10 INTER
## 3572 1 10 INTER
## 3573 1 2 INTER
## 3574 1 2 INTER
## 3575 1 2 INTER
## 3576 1 2 INTER
## 3577 1 4 INTER
## 3578 1 4 INTER
## 3579 1 4 INTER
## 3580 1 4 INTER
## 3581 1 10 INTER
## 3582 1 10 INTER
## 3583 1 10 INTER
## 3584 1 10 INTER
## 3585 1 2 INTER
## 3586 1 2 INTER
## 3587 1 2 INTER
## 3588 1 2 INTER
## 3589 1 4 INTER
## 3590 1 4 INTER
## 3591 1 4 INTER
## 3592 1 4 INTER
## 3593 1 10 INTER
## 3594 1 10 INTER
## 3595 1 10 INTER
## 3596 1 10 INTER
## 3597 1 2 INTER
## 3598 1 2 INTER
## 3599 1 2 INTER
## 3600 1 2 INTER
## 3601 1 4 INTER
## 3602 1 4 INTER
## 3603 1 4 INTER
## 3604 1 4 INTER
## 3605 1 10 INTER
## 3606 1 10 INTER
## 3607 1 10 INTER
## 3608 1 10 INTER
## 3609 1 2 INTER
## 3610 1 2 INTER
## 3611 1 2 INTER
## 3612 1 2 INTER
## 3613 1 4 INTER
## 3614 1 4 INTER
## 3615 1 4 INTER
## 3616 1 4 INTER
## 3617 1 10 INTER
## 3618 1 10 INTER
## 3619 1 10 INTER
## 3620 1 10 INTER
## 3621 1 2 INTER
## 3622 1 2 INTER
## 3623 1 2 INTER
## 3624 1 2 INTER
## 3625 1 4 INTER
## 3626 1 4 INTER
## 3627 1 4 INTER
## 3628 1 4 INTER
## 3629 1 10 INTER
## 3630 1 10 INTER
## 3631 1 10 INTER
## 3632 1 10 INTER
## 3633 1 1 INTRA
## 3634 1 1 INTRA
## 3635 1 1 INTRA
## 3636 1 1 INTRA
## 3637 2 2 INTRA
## 3638 2 2 INTRA
## 3639 2 2 INTRA
## 3640 2 2 INTRA
## 3641 4 4 INTRA
## 3642 4 4 INTRA
## 3643 4 4 INTRA
## 3644 4 4 INTRA
## 3645 10 10 INTRA
## 3646 10 10 INTRA
## 3647 10 10 INTRA
## 3648 10 10 INTRA
## 3649 1 1 INTRA
## 3650 1 1 INTRA
## 3651 1 1 INTRA
## 3652 1 1 INTRA
## 3653 2 2 INTRA
## 3654 2 2 INTRA
## 3655 2 2 INTRA
## 3656 2 2 INTRA
## 3657 4 4 INTRA
## 3658 4 4 INTRA
## 3659 4 4 INTRA
## 3660 4 4 INTRA
## 3661 10 10 INTRA
## 3662 10 10 INTRA
## 3663 10 10 INTRA
## 3664 10 10 INTRA
## 3665 1 1 INTRA
## 3666 1 1 INTRA
## 3667 1 1 INTRA
## 3668 1 1 INTRA
## 3669 2 2 INTRA
## 3670 2 2 INTRA
## 3671 2 2 INTRA
## 3672 2 2 INTRA
## 3673 4 4 INTRA
## 3674 4 4 INTRA
## 3675 4 4 INTRA
## 3676 4 4 INTRA
## 3677 10 10 INTRA
## 3678 10 10 INTRA
## 3679 10 10 INTRA
## 3680 10 10 INTRAca$GrowthRateOA<-sapply(c(1:length(ca[,1])), function(x){
#print(x)
if(ca$Focal_Female[x]=="Tu"){
a<-ca$TuFemales[x]/ca$DensFocal[x]
}else if(ca$Focal_Female[x]=="Te"){
a<-ca$TeFemales[x]/ca$DensFocal[x]
}else
a<-NA
a
})
#Growth rate per day
ca$GrowthRatePD<-sapply(c(1:dim(ca)[1]), function(x){
#print(x)
if(ca$Focal_Female[x]=="Tu"){
a<-(ca$TuFemales[x]/ca$DensFocal[x])/3
}else if(ca$Focal_Female[x]=="Te"){
a<-(ca$TeFemales[x]/ca$DensFocal[x])/3
}else{
a<-NA}
a
})Importing coexistence
setwd("./Repository/For_repository/")
getwd()## [1] "/Volumes/IF/Desktop/Project_manuscript/Coexistence_cadmium/Repository/For_repository"coex_g42<-read.csv("./Data/Coexistence Cd_G42_checked.csv", header=TRUE) # Data from the coexistence experiment
coex_g42$Rep2<-as.factor(coex_g42$Rep)
coex_g42$X1st.pair<-as.factor(coex_g42$X1st_pair)
coex_g42$X2nd.pair<-as.factor(coex_g42$X2nd_pair)
coex_g42$SRTu<-as.factor(coex_g42$SRTu)
coex_g42$SRTe<-as.factor(coex_g42$SRTe)
coex_g42$Box2<-as.factor(coex_g42$Box)
### summary data per leaf (because the leaflets are not attributable)
coex_g42_res<-gather(coex_g42, leaf, females, Leaf_2_Up_Tu:Leaf_5_Down_Te, factor_key=TRUE)
str(coex_g42_res)## 'data.frame': 43200 obs. of 16 variables:
## $ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Block1 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ X1st_pair : chr "2.4" "2.4" "2.4" "2.4" ...
## $ X2nd_pair : chr "3.5" "3.5" "3.5" "3.5" ...
## $ Env : chr "water" "water" "water" "water" ...
## $ Box : int 6 6 6 6 6 7 7 7 7 7 ...
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 2 2 2 2 2 2 2 2 2 2 ...
## $ Leaflet : int 1 2 3 4 5 1 2 3 4 5 ...
## $ Observations: chr NA NA NA NA ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ X1st.pair : Factor w/ 8 levels "2.4","24c","2c4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ X2nd.pair : Factor w/ 10 levels "1.4","2.4","24c",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Box2 : Factor w/ 10 levels "1","2","3","4",..: 6 6 6 6 6 7 7 7 7 7 ...
## $ leaf : Factor w/ 16 levels "Leaf_2_Up_Tu",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ females : int 0 1 2 NA NA 1 2 0 NA NA ...coex_g42_res$char<-as.character(coex_g42_res$leaf)
aux4<-as.data.frame(t(as.data.frame(sapply(c(1:length(coex_g42_res$Rep)), function(x){
a<-strsplit(coex_g42_res$char[x], split="_")[[1]]
c(a[2:4])
}))))
colnames(aux4)<-c("Leaf2", "Direction", "Species")
coex_g42_res<-cbind(coex_g42_res, aux4)
str(coex_g42_res)## 'data.frame': 43200 obs. of 20 variables:
## $ Rep : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Block1 : int 2 2 2 2 2 2 2 2 2 2 ...
## $ X1st_pair : chr "2.4" "2.4" "2.4" "2.4" ...
## $ X2nd_pair : chr "3.5" "3.5" "3.5" "3.5" ...
## $ Env : chr "water" "water" "water" "water" ...
## $ Box : int 6 6 6 6 6 7 7 7 7 7 ...
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 2 2 2 2 2 2 2 2 2 2 ...
## $ Leaflet : int 1 2 3 4 5 1 2 3 4 5 ...
## $ Observations: chr NA NA NA NA ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ X1st.pair : Factor w/ 8 levels "2.4","24c","2c4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ X2nd.pair : Factor w/ 10 levels "1.4","2.4","24c",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Box2 : Factor w/ 10 levels "1","2","3","4",..: 6 6 6 6 6 7 7 7 7 7 ...
## $ leaf : Factor w/ 16 levels "Leaf_2_Up_Tu",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ females : int 0 1 2 NA NA 1 2 0 NA NA ...
## $ char : chr "Leaf_2_Up_Tu" "Leaf_2_Up_Tu" "Leaf_2_Up_Tu" "Leaf_2_Up_Tu" ...
## $ Leaf2 : chr "2" "2" "2" "2" ...
## $ Direction : chr "Up" "Up" "Up" "Up" ...
## $ Species : chr "Tu" "Tu" "Tu" "Tu" ...sum_coex_g42<-coex_g42_res %>%
group_by(Rep2, SRTu, SRTe, Box2, Leaf2,X1st.pair,X2nd.pair, Direction, Species, Env) %>%
summarize(av_females=sum(females, na.rm=TRUE))## `summarise()` has grouped output by 'Rep2', 'SRTu', 'SRTe', 'Box2', 'Leaf2',
## 'X1st.pair', 'X2nd.pair', 'Direction', 'Species'. You can override using the
## `.groups` argument.sum_coex_g42## # A tibble: 8,640 × 11
## # Groups: Rep2, SRTu, SRTe, Box2, Leaf2, X1st.pair, X2nd.pair, Direction,
## # Species [5,936]
## Rep2 SRTu SRTe Box2 Leaf2 X1st.pair X2nd.pair Direction Species Env
## <fct> <fct> <fct> <fct> <chr> <fct> <fct> <chr> <chr> <chr>
## 1 1 Tu1 Te4 1 2 2.4 3.5 Down Te Cd
## 2 1 Tu1 Te4 1 2 2.4 3.5 Down Te water
## 3 1 Tu1 Te4 1 2 2.4 3.5 Down Tu Cd
## 4 1 Tu1 Te4 1 2 2.4 3.5 Down Tu water
## 5 1 Tu1 Te4 1 2 2.4 3.5 Up Te Cd
## 6 1 Tu1 Te4 1 2 2.4 3.5 Up Te water
## 7 1 Tu1 Te4 1 2 2.4 3.5 Up Tu Cd
## 8 1 Tu1 Te4 1 2 2.4 3.5 Up Tu water
## 9 1 Tu1 Te4 1 2 24c 3c5 Down Te Heteroge…
## 10 1 Tu1 Te4 1 2 24c 3c5 Down Tu Heteroge…
## # ℹ 8,630 more rows
## # ℹ 1 more variable: av_females <int>sum_coex_g42$Direction<-as.factor(sum_coex_g42$Direction)
sum_coex_g42_res<-as.data.frame(spread(sum_coex_g42, key=Species, value=av_females))
sum_coex_g42_res2<-sum_coex_g42_res %>%
group_by(Rep2, SRTu, SRTe, Box2, Leaf2,X1st.pair,X2nd.pair, Env) %>%
summarize(av_Te=sum(Te, na.rm=TRUE), av_Tu=sum(Tu, na.rm=TRUE)) %>% as.data.frame()## `summarise()` has grouped output by 'Rep2', 'SRTu', 'SRTe', 'Box2', 'Leaf2',
## 'X1st.pair', 'X2nd.pair'. You can override using the `.groups` argument.str(sum_coex_g42_res2)## 'data.frame': 2160 obs. of 10 variables:
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 1 1 1 1 1 ...
## $ Box2 : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Leaf2 : chr "2" "2" "2" "3" ...
## $ X1st.pair: Factor w/ 8 levels "2.4","24c","2c4",..: 1 1 2 1 1 2 1 1 2 1 ...
## $ X2nd.pair: Factor w/ 10 levels "1.4","2.4","24c",..: 5 5 7 5 5 7 5 5 7 5 ...
## $ Env : chr "Cd" "water" "Heterogeneous" "Cd" ...
## $ av_Te : int 85 0 0 90 0 0 18 6 0 206 ...
## $ av_Tu : int 11 1 0 2 1 0 0 7 0 22 ...sum_coex_g42_res2<-sum_coex_g42_res2[-which(sum_coex_g42_res2$Env=="Heterogeneous"),]
coex_g42_rep<-sum_coex_g42_res2 %>%
group_by(Rep2, Leaf2, SRTu, SRTe, Env, Box2) %>%
summarize( sum_Te=sum(av_Te, na.rm=TRUE), sum_Tu=mean(av_Tu, na.rm=TRUE)) %>% as.data.frame()## `summarise()` has grouped output by 'Rep2', 'Leaf2', 'SRTu', 'SRTe', 'Env'. You
## can override using the `.groups` argument.coex_g42_rep$Env[which(coex_g42_rep$Env=="Cd")]<-"Cd" 2 - Estimate parameters from cxr package
cxr accepts a data frame with a first column called fitness with positive values and numeric columns with number of individuals. Each row is one individual. For multiple species the easier is to create a list, each with a data frame that has in the first column number of individuals produced and then the number of neighbours
this case we transformed all 0s into 1 (so that the log is 0) For that we need to add +1 to all data so that the variance is not changed.
Note that the files of the output data are available in the folder. To avoid having the run the code again I added eval = FALSE. To use the already generated ouptut files you just need to import the data. To generate the data again, you need to change eval=TRUE.
2.1 - Estimate All replicates together
normal
rows in the alpha element of the returning list correspond to species i and columns to species j for each αij coefficient.
data table summary
Cadmium
data table summary
joining data frame
Importing data files
To use the data sets already available in the repository, we can simply read the csv.
## Importing
param_all_REP<-read.csv("./Analyses/cxr_normal_REP/parameters_cxr_normal_REP.csv")
param_all_REP_upper<-read.csv("./Analyses/cxr_normal_REP/parameters_cxr_normal_REP_upper.csv")
param_all_REP_lower<-read.csv( "./Analyses/cxr_normal_REP/parameters_cxr_normal_REP_lower.csv")
param_all_REP<-param_all_REP[,-1]
param_all_REP_upper<-param_all_REP_upper[,-1]
param_all_REP_lower<-param_all_REP_lower[,-1]Data wrangling to make it easier to plot the figures
param_all_REP_long<-gather(param_all_REP, parameter, value,Tu_lambda:Te_inter )
param_all_REP_long$category<-mapvalues(param_all_REP_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
param_all_REP_lower_long<-gather(param_all_REP_lower, parameter, value,Tu_lambda:Te_inter )
param_all_REP_lower_long$category<-mapvalues(param_all_REP_lower_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
param_all_REP_upper_long<-gather(param_all_REP_upper, parameter, value,Tu_lambda:Te_inter )
param_all_REP_upper_long$category<-mapvalues(param_all_REP_upper_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
colnames(param_all_REP_lower_long)[5]<-"lower"
colnames(param_all_REP_upper_long)[5]<-"upper"
str(param_all_REP_long)## 'data.frame': 48 obs. of 6 variables:
## $ Tu_Regime : chr "SR1" "SR2" "SR1" "SR2" ...
## $ Te_Regime : chr "SR4" "SR4" "SR5" "SR5" ...
## $ Environment: chr "N" "N" "N" "N" ...
## $ parameter : chr "Tu_lambda" "Tu_lambda" "Tu_lambda" "Tu_lambda" ...
## $ value : num 2.48 2.55 2.48 2.55 1.22 ...
## $ category : chr "lambda" "lambda" "lambda" "lambda" ...param_all_REP_long<-cbind(param_all_REP_long[,1:6],param_all_REP_lower_long$lower, param_all_REP_upper_long$upper)
colnames(param_all_REP_long)[7:8]<-c("lower","upper")2.2 - Estimate each replicate separately
normal
rows in the alpha element of the returning list correspond to species i and columns to species j for each αij coefficient.
data table summary
Cadmium
data table summary
Again, to use the files already done we can just import from the available files
Importing data files
## Importing
param_all_w0<-read.csv("./Analyses/cxr_normal/parameters_cxr_normal.csv")
param_all_w0_upper<-read.csv("./Analyses/cxr_normal/parameters_cxr_normal_upper.csv")
param_all_w0_lower<-read.csv( "./Analyses/cxr_normal/parameters_cxr_normal_lower.csv")
param_all_w0<-param_all_w0[,-1]
param_all_w0_upper<-param_all_w0_upper[,-1]
param_all_w0_lower<-param_all_w0_lower[,-1]data wrangling
param_all_w0_long<-gather(param_all_w0, parameter, value,Tu_lambda:Te_inter )
param_all_w0_long$category<-mapvalues(param_all_w0_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
param_all_w0_lower_long<-gather(param_all_w0_lower, parameter, value,Tu_lambda:Te_inter )
param_all_w0_lower_long$category<-mapvalues(param_all_w0_lower_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
param_all_w0_upper_long<-gather(param_all_w0_upper, parameter, value,Tu_lambda:Te_inter )
param_all_w0_upper_long$category<-mapvalues(param_all_w0_upper_long$parameter, c("Tu_lambda", "Te_lambda", "Tu_intra", "Te_intra","Tu_inter", "Te_inter"), c("lambda", "lambda", "intra", "intra", "inter", "inter"))
colnames(param_all_w0_lower_long)[6]<-"lower"
colnames(param_all_w0_upper_long)[6]<-"upper"
str(param_all_w0_long)## 'data.frame': 216 obs. of 7 variables:
## $ Tu_Regime : chr "SR1" "SR2" "SR1" "SR2" ...
## $ Te_Regime : chr "SR4" "SR4" "SR5" "SR5" ...
## $ Replicate : int 1 1 1 1 2 2 3 3 3 3 ...
## $ Environment: chr "N" "N" "N" "N" ...
## $ parameter : chr "Tu_lambda" "Tu_lambda" "Tu_lambda" "Tu_lambda" ...
## $ value : num 2.52 1.9 2.52 1.9 2.03 ...
## $ category : chr "lambda" "lambda" "lambda" "lambda" ...param_all_w0_long<-cbind(param_all_w0_long[,1:6],param_all_w0_lower_long$lower, param_all_w0_upper_long$upper)
colnames(param_all_w0_long)[7:8]<-c("lower","upper")
str(param_all_w0_long)## 'data.frame': 216 obs. of 8 variables:
## $ Tu_Regime : chr "SR1" "SR2" "SR1" "SR2" ...
## $ Te_Regime : chr "SR4" "SR4" "SR5" "SR5" ...
## $ Replicate : int 1 1 1 1 2 2 3 3 3 3 ...
## $ Environment: chr "N" "N" "N" "N" ...
## $ parameter : chr "Tu_lambda" "Tu_lambda" "Tu_lambda" "Tu_lambda" ...
## $ value : num 2.52 1.9 2.52 1.9 2.03 ...
## $ lower : num 2.15 1.67 2.15 1.67 1.77 ...
## $ upper : num 2.88 2.13 2.88 2.13 2.29 ...2.3 - Testing differences in parameters (estimated)
2.3.1 - Distribution
str(param_all_w0)## 'data.frame': 36 obs. of 10 variables:
## $ Tu_Regime : chr "SR1" "SR2" "SR1" "SR2" ...
## $ Te_Regime : chr "SR4" "SR4" "SR5" "SR5" ...
## $ Replicate : int 1 1 1 1 2 2 3 3 3 3 ...
## $ Environment: chr "N" "N" "N" "N" ...
## $ Tu_lambda : num 2.52 1.9 2.52 1.9 2.03 ...
## $ Te_lambda : num 4.9 4.9 5.16 5.16 5.12 ...
## $ Tu_intra : num 0.05078 -0.01634 0.05078 -0.01634 0.00422 ...
## $ Te_intra : num 0.0186 0.0186 0.0419 0.0419 -0.0236 ...
## $ Tu_inter : num 0.03981 0.01356 0.04432 0.03235 0.00206 ...
## $ Te_inter : num 0.1021 0.0463 0.0591 0.1046 0.2204 ...descdist(param_all_w0$Tu_lambda, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: 1.098786 max: 3.512233
## median: 1.730981
## mean: 1.935727
## estimated sd: 0.7423809
## estimated skewness: 0.6858225
## estimated kurtosis: 2.336732descdist(param_all_w0$Te_lambda, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: 1.363013 max: 8.088399
## median: 3.140028
## mean: 3.69777
## estimated sd: 1.868651
## estimated skewness: 0.5471405
## estimated kurtosis: 2.492893descdist(param_all_w0$Tu_intra, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: -0.01664566 max: 0.0776862
## median: 0.01601209
## mean: 0.0195158
## estimated sd: 0.02648953
## estimated skewness: 0.5018871
## estimated kurtosis: 2.622023descdist(param_all_w0$Te_intra, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: -0.0236172 max: 0.05467569
## median: 0.02940243
## mean: 0.02701788
## estimated sd: 0.02220119
## estimated skewness: -0.6117063
## estimated kurtosis: 2.445291descdist(param_all_w0$Tu_inter, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: -0.0401219 max: 0.07363919
## median: 0.0218683
## mean: 0.02021898
## estimated sd: 0.02804374
## estimated skewness: -0.05890052
## estimated kurtosis: 2.576286descdist(param_all_w0$Te_inter, discrete=FALSE, boot=1000)
## summary statistics
## ------
## min: -0.05584114 max: 0.3147787
## median: 0.04266622
## mean: 0.04939625
## estimated sd: 0.070847
## estimated skewness: 1.784989
## estimated kurtosis: 8.103329hist(param_all_w0$Te_lambda)
hist(param_all_w0$Tu_intra)
hist(param_all_w0$Te_intra)
hist(param_all_w0$Tu_inter)
hist(param_all_w0$Te_inter)
2.3.2 - Does cadmium change parameters?
gr_tu_cd_1<-glmmTMB(Tu_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ))
gr_tu_cd_2<-glmmTMB(Tu_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=Gamma(link="log"))
gr_tu_cd_3<-glmmTMB(Tu_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=gaussian(link="log"))
anova(gr_tu_cd_1,gr_tu_cd_2,gr_tu_cd_3)## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
## Models:
## gr_tu_cd_1: Tu_lambda ~ Environment, zi=~0, disp=~1
## gr_tu_cd_2: Tu_lambda ~ Environment, zi=~0, disp=~1
## gr_tu_cd_3: Tu_lambda ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_tu_cd_1 3 11.5117 12.4194 -2.75584 5.5117
## gr_tu_cd_2 3 5.3421 6.2499 0.32893 -0.6579 6.1695 0 <2e-16 ***
## gr_tu_cd_3 3 11.5117 12.4194 -2.75584 5.5117 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_tu_cd_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 5.3 6.2 0.3 -0.7 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0181
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2006 0.0601 3.338 0.000844 ***
## EnvironmentN 0.7211 0.0850 8.484 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_tu_cd_2, plot = F)
plot(simulationOutput)## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
#No problems
gr_te_cd_1<-glmmTMB(Te_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ))
gr_te_cd_2<-glmmTMB(Te_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=Gamma(link="log"))
gr_te_cd_3<-glmmTMB(Te_lambda~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=gaussian(link="log"))
anova(gr_te_cd_1,gr_te_cd_2,gr_te_cd_3)## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
## Models:
## gr_te_cd_1: Te_lambda ~ Environment, zi=~0, disp=~1
## gr_te_cd_2: Te_lambda ~ Environment, zi=~0, disp=~1
## gr_te_cd_3: Te_lambda ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_te_cd_1 3 31.497 32.405 -12.7485 25.497
## gr_te_cd_2 3 22.106 23.014 -8.0531 16.106 9.3908 0 <2e-16 ***
## gr_te_cd_3 3 31.497 32.405 -12.7485 25.497 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_cd_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 22.1 23.0 -8.1 16.1 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0298
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.56092 0.07726 7.26 3.87e-13 ***
## EnvironmentN 1.18262 0.10926 10.82 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_te_cd_2, plot = F)
plot(simulationOutput)# No problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
########
intra_tu_cd_1<-glmmTMB(Tu_intra~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ))
intra_tu_cd_2<-glmmTMB(Tu_intra+1~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=Gamma(link="log"))
intra_tu_cd_3<-glmmTMB(Tu_intra+1~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=gaussian(link="log"))
anova(intra_tu_cd_1,intra_tu_cd_2,intra_tu_cd_3)## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
## Models:
## intra_tu_cd_1: Tu_intra ~ Environment, zi=~0, disp=~1
## intra_tu_cd_2: Tu_intra + 1 ~ Environment, zi=~0, disp=~1
## intra_tu_cd_3: Tu_intra + 1 ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_tu_cd_1 3 -40.051 -39.144 23.026 -46.051
## intra_tu_cd_2 3 -40.248 -39.340 23.124 -46.248 0.1966 0 <2e-16 ***
## intra_tu_cd_3 3 -40.051 -39.144 23.026 -46.051 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_cd_1) ## Family: gaussian ( identity )
## Formula: Tu_intra ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -40.1 -39.1 23.0 -46.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000586
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009066 0.010821 0.838 0.402
## EnvironmentN 0.019251 0.015304 1.258 0.208summary(intra_tu_cd_2) #### But added +1 to all data## Family: Gamma ( log )
## Formula: Tu_intra + 1 ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -40.2 -39.3 23.1 -46.2 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.000553
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009025 0.010521 0.858 0.391
## EnvironmentN 0.018898 0.014879 1.270 0.204simulationOutput <- simulateResiduals(fittedModel = intra_tu_cd_1, plot = F)
plot(simulationOutput)# No problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
#Gamma and gaussian give very similar estimates and values
intra_te_cd_1<-glmmTMB(Te_intra~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ))
intra_te_cd_2<-glmmTMB(Te_intra+1~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=Gamma(link="log"))
intra_te_cd_3<-glmmTMB(Te_intra+1~Environment, data=subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" ), family=gaussian(link="log"))
anova(intra_te_cd_1,intra_te_cd_2,intra_te_cd_3)## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
## Models:
## intra_te_cd_1: Te_intra ~ Environment, zi=~0, disp=~1
## intra_te_cd_2: Te_intra + 1 ~ Environment, zi=~0, disp=~1
## intra_te_cd_3: Te_intra + 1 ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_te_cd_1 3 -43.086 -42.178 24.543 -49.086
## intra_te_cd_2 3 -43.087 -42.180 24.544 -49.087 0.0019 0 <2e-16 ***
## intra_te_cd_3 3 -43.086 -42.178 24.543 -49.086 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_cd_2)## Family: Gamma ( log )
## Formula: Te_intra + 1 ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.1 -42.2 24.5 -49.1 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.000422
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005125 0.009189 0.558 0.577
## EnvironmentN 0.013499 0.012995 1.039 0.299summary(intra_te_cd_1) #Again very similar estimates## Family: gaussian ( identity )
## Formula: Te_intra ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.1 -42.2 24.5 -49.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000432
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005138 0.009298 0.553 0.581
## EnvironmentN 0.013660 0.013150 1.039 0.299simulationOutput <- simulateResiduals(fittedModel = intra_te_cd_1, plot = F)
plot(simulationOutput) #No problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
######
inter_tu_cd_1<-glmmTMB(Tu_inter~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")))
inter_tu_cd_2<-glmmTMB(Tu_inter+1~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")), family=Gamma(link="log"))
inter_tu_cd_3<-glmmTMB(Tu_inter+1~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")), family=gaussian(link="log"))
anova(inter_tu_cd_1,inter_tu_cd_2,inter_tu_cd_3)## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
## Models:
## inter_tu_cd_1: Tu_inter ~ Environment, zi=~0, disp=~1
## inter_tu_cd_2: Tu_inter + 1 ~ Environment, zi=~0, disp=~1
## inter_tu_cd_3: Tu_inter + 1 ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_tu_cd_1 3 -44.339 -43.431 25.169 -50.339
## inter_tu_cd_2 3 -44.390 -43.483 25.195 -50.390 0.0518 0 <2e-16 ***
## inter_tu_cd_3 3 -44.339 -43.431 25.169 -50.339 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_tu_cd_2)## Family: Gamma ( log )
## Formula: Tu_inter + 1 ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -44.4 -43.5 25.2 -50.4 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.000361
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.010748 0.008495 1.265 0.2058
## EnvironmentN 0.028916 0.012014 2.407 0.0161 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_tu_cd_1)## Family: gaussian ( identity )
## Formula: Tu_inter ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -44.3 -43.4 25.2 -50.3 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000381
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.010806 0.008734 1.237 0.2160
## EnvironmentN 0.029655 0.012351 2.401 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = inter_tu_cd_1, plot = F)
plot(simulationOutput) #no problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
inter_te_cd_1<-glmmTMB(Te_inter~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")))
inter_te_cd_2<-glmmTMB(Te_inter+1~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")), family=Gamma(link="log"))
inter_te_cd_3<-glmmTMB(Te_inter+1~Environment, data=subset(param_all_w0, (Tu_Regime=="SR1" & Te_Regime=="SR4")), family=gaussian(link="log"))
anova(inter_te_cd_1,inter_te_cd_2,inter_te_cd_3)## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
## Models:
## inter_te_cd_1: Te_inter ~ Environment, zi=~0, disp=~1
## inter_te_cd_2: Te_inter + 1 ~ Environment, zi=~0, disp=~1
## inter_te_cd_3: Te_inter + 1 ~ Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_te_cd_1 3 -26.108 -25.20 16.054 -32.108
## inter_te_cd_2 3 -26.898 -25.99 16.449 -32.898 0.7904 0 <2e-16 ***
## inter_te_cd_3 3 -26.108 -25.20 16.054 -32.108 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_cd_2)## Family: Gamma ( log )
## Formula: Te_inter + 1 ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -26.9 -26.0 16.4 -32.9 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.002
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01309 0.02000 -0.655 0.513
## EnvironmentN 0.11493 0.02828 4.064 4.82e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_cd_1)## Family: gaussian ( identity )
## Formula: Te_inter ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -26.1 -25.2 16.1 -32.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.00236
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01300 0.02173 -0.598 0.55
## EnvironmentN 0.12021 0.03073 3.912 9.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = inter_te_cd_1, plot = F)
plot(simulationOutput) #No problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
Summary
summary(gr_tu_cd_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 5.3 6.2 0.3 -0.7 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0181
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2006 0.0601 3.338 0.000844 ***
## EnvironmentN 0.7211 0.0850 8.484 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_cd_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 22.1 23.0 -8.1 16.1 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0298
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.56092 0.07726 7.26 3.87e-13 ***
## EnvironmentN 1.18262 0.10926 10.82 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_cd_1) ## Family: gaussian ( identity )
## Formula: Tu_intra ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -40.1 -39.1 23.0 -46.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000586
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009066 0.010821 0.838 0.402
## EnvironmentN 0.019251 0.015304 1.258 0.208summary(intra_te_cd_1)## Family: gaussian ( identity )
## Formula: Te_intra ~ Environment
## Data: subset(param_all_w0, Tu_Regime == "SR1" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.1 -42.2 24.5 -49.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000432
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005138 0.009298 0.553 0.581
## EnvironmentN 0.013660 0.013150 1.039 0.299summary(inter_tu_cd_1)## Family: gaussian ( identity )
## Formula: Tu_inter ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -44.3 -43.4 25.2 -50.3 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000381
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.010806 0.008734 1.237 0.2160
## EnvironmentN 0.029655 0.012351 2.401 0.0163 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_cd_1)## Family: gaussian ( identity )
## Formula: Te_inter ~ Environment
## Data: subset(param_all_w0, (Tu_Regime == "SR1" & Te_Regime == "SR4"))
##
## AIC BIC logLik deviance df.resid
## -26.1 -25.2 16.1 -32.1 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.00236
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01300 0.02173 -0.598 0.55
## EnvironmentN 0.12021 0.03073 3.912 9.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(gr_tu_cd_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_lambda
## Chisq Df Pr(>Chisq)
## Environment 71.985 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(gr_te_cd_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_lambda
## Chisq Df Pr(>Chisq)
## Environment 117.15 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(intra_tu_cd_1) ## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_intra
## Chisq Df Pr(>Chisq)
## Environment 1.5823 1 0.2084Anova(intra_te_cd_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_intra
## Chisq Df Pr(>Chisq)
## Environment 1.0791 1 0.2989Anova(inter_tu_cd_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_inter
## Chisq Df Pr(>Chisq)
## Environment 5.7649 1 0.01635 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(inter_te_cd_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_inter
## Chisq Df Pr(>Chisq)
## Environment 15.3 1 9.17e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 12.3.3 - Does evolution change the performance?
gr_tu_ev_1<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="Cd" & Te_Regime=="SR4"))
gr_tu_ev_2<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4"), family=Gamma(link="log"))
gr_tu_ev_3<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4"), family=gaussian(link="log"))
anova(gr_tu_ev_1,gr_tu_ev_2,gr_tu_ev_3)## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
## Models:
## gr_tu_ev_1: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## gr_tu_ev_2: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## gr_tu_ev_3: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_tu_ev_1 3 -7.6268 -7.0351 6.8134 -13.627
## gr_tu_ev_2 3 -7.8489 -7.2572 6.9244 -13.849 0.2221 0 <2e-16 ***
## gr_tu_ev_3 3 -7.6268 -7.0351 6.8134 -13.627 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_tu_ev_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -7.8 -7.3 6.9 -13.8 6
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.00744
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20062 0.03858 5.201 1.98e-07 ***
## Tu_RegimeSR2 0.14393 0.05786 2.487 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_tu_ev_1)## Family: gaussian ( identity )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -7.6 -7.0 6.8 -13.6 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.0129
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.22216 0.05076 24.079 <2e-16 ***
## Tu_RegimeSR2 0.18919 0.07614 2.485 0.013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_tu_ev_2, plot = F)
plot(simulationOutput) #no problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
gr_te_ev_1<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"))
gr_te_ev_2<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"), family=Gamma(link="log"))
gr_te_ev_3<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"), family=gaussian(link="log"))
anova(gr_te_ev_1,gr_te_ev_2,gr_te_ev_3)## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
## Models:
## gr_te_ev_1: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## gr_te_ev_2: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## gr_te_ev_3: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_te_ev_1 3 12.699 13.607 -3.3494 6.6989
## gr_te_ev_2 3 12.238 13.146 -3.1192 6.2384 0.4605 0 <2e-16 ***
## gr_te_ev_3 3 12.699 13.607 -3.3494 6.6989 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_ev_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## 12.2 13.1 -3.1 6.2 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0289
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.56092 0.07602 7.379 1.6e-13 ***
## Te_RegimeSR5 0.22763 0.10751 2.117 0.0342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_ev_1)## Family: gaussian ( identity )
## Formula: Te_lambda ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## 12.7 13.6 -3.3 6.7 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.114
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7523 0.1513 11.584 <2e-16 ***
## Te_RegimeSR5 0.4479 0.2139 2.094 0.0363 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_te_ev_2, plot = F)
plot(simulationOutput)# No problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
## intra
intra_tu_ev_1<-glmmTMB(Tu_intra~Tu_Regime, data=subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4"))
intra_tu_ev_2<-glmmTMB(Tu_intra+1~Tu_Regime, data=subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4"), family=Gamma(link="log"))
intra_tu_ev_3<-glmmTMB(Tu_intra+1~Tu_Regime, data=subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4"), family=gaussian(link="log"))
anova(intra_tu_ev_1,intra_tu_ev_2,intra_tu_ev_3)## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
## Models:
## intra_tu_ev_1: Tu_intra ~ Tu_Regime, zi=~0, disp=~1
## intra_tu_ev_2: Tu_intra + 1 ~ Tu_Regime, zi=~0, disp=~1
## intra_tu_ev_3: Tu_intra + 1 ~ Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_tu_ev_1 3 -43.401 -42.809 24.700 -49.401
## intra_tu_ev_2 3 -43.426 -42.835 24.713 -49.426 0.0259 0 <2e-16 ***
## intra_tu_ev_3 3 -43.401 -42.809 24.700 -49.401 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_ev_2)## Family: Gamma ( log )
## Formula: Tu_intra + 1 ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.4 -42.8 24.7 -49.4 6
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.000236
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009025 0.006873 1.313 0.189
## Tu_RegimeSR2 0.003735 0.010309 0.362 0.717summary(intra_tu_ev_1)## Family: gaussian ( identity )
## Formula: Tu_intra ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.4 -42.8 24.7 -49.4 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000242
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009066 0.006956 1.303 0.192
## Tu_RegimeSR2 0.003776 0.010434 0.362 0.717simulationOutput <- simulateResiduals(fittedModel = intra_tu_ev_1, plot = F)
plot(simulationOutput) #no problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
intra_te_ev_1<-glmmTMB(Te_intra~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"))
intra_te_ev_2<-glmmTMB(Te_intra+1~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"), family=Gamma(link="log"))
intra_te_ev_3<-glmmTMB(Te_intra+1~Te_Regime, data=subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1"), family=gaussian(link="log"))
anova(intra_te_ev_1,intra_te_ev_2,intra_te_ev_3)## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
## Models:
## intra_te_ev_1: Te_intra ~ Te_Regime, zi=~0, disp=~1
## intra_te_ev_2: Te_intra + 1 ~ Te_Regime, zi=~0, disp=~1
## intra_te_ev_3: Te_intra + 1 ~ Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_te_ev_1 3 -49.045 -48.137 27.522 -55.045
## intra_te_ev_2 3 -49.035 -48.127 27.518 -55.035 0.0000 0 1
## intra_te_ev_3 3 -49.045 -48.137 27.522 -55.045 0.0094 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_ev_3)## Family: gaussian ( log )
## Formula: Te_intra + 1 ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -49.0 -48.1 27.5 -55.0 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005125 0.006867 0.746 0.45546
## Te_RegimeSR5 0.028033 0.009578 2.927 0.00342 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_ev_1)## Family: gaussian ( identity )
## Formula: Te_intra ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -49.0 -48.1 27.5 -55.0 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005138 0.006902 0.744 0.45661
## Te_RegimeSR5 0.028576 0.009761 2.928 0.00342 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = intra_te_ev_1, plot = F)
plot(simulationOutput) #no problems## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
## inter
inter_tu_ev_1<-glmmTMB(Tu_inter~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="Cd"))
inter_tu_ev_2<-glmmTMB(Tu_inter+1~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="Cd"), family=Gamma(link="log"))
inter_tu_ev_3<-glmmTMB(Tu_inter+1~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="Cd"), family=gaussian(link="log"))
anova(inter_tu_ev_1,inter_tu_ev_2,inter_tu_ev_3)## Data: subset(param_all_w0, Environment == "Cd")
## Models:
## inter_tu_ev_1: Tu_inter ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## inter_tu_ev_2: Tu_inter + 1 ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## inter_tu_ev_3: Tu_inter + 1 ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_tu_ev_1 5 -87.807 -83.355 48.903 -97.807
## inter_tu_ev_2 5 -87.725 -83.273 48.863 -97.725 0.0000 0 1
## inter_tu_ev_3 5 -87.807 -83.355 48.903 -97.807 0.0819 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_tu_ev_3)## Family: gaussian ( log )
## Formula: Tu_inter + 1 ~ Tu_Regime * Te_Regime
## Data: subset(param_all_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## -87.8 -83.4 48.9 -97.8 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000256
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.010748 0.007074 1.519 0.129
## Tu_RegimeSR2 0.003855 0.010589 0.364 0.716
## Te_RegimeSR5 -0.011809 0.010064 -1.173 0.241
## Tu_RegimeSR2:Te_RegimeSR5 0.005246 0.015042 0.349 0.727summary(inter_tu_ev_1)## Family: gaussian ( identity )
## Formula: Tu_inter ~ Tu_Regime * Te_Regime
## Data: subset(param_all_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## -87.8 -83.4 48.9 -97.8 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000256
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.010806 0.007151 1.511 0.131
## Tu_RegimeSR2 0.003905 0.010726 0.364 0.716
## Te_RegimeSR5 -0.011866 0.010113 -1.173 0.241
## Tu_RegimeSR2:Te_RegimeSR5 0.005228 0.015169 0.345 0.730simulationOutput <- simulateResiduals(fittedModel = inter_tu_ev_1, plot = F)
plot(simulationOutput) #no problem
pairs(emmeans(inter_tu_ev_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 0.01187 0.0101 13 1.173 0.2617
## SR4 SR1 - SR4 SR2 -0.00390 0.0107 13 -0.364 0.7217
## SR4 SR1 - SR5 SR2 0.00273 0.0107 13 0.255 0.8029
## SR5 SR1 - SR4 SR2 -0.01577 0.0107 13 -1.470 0.1653
## SR5 SR1 - SR5 SR2 -0.00913 0.0107 13 -0.851 0.4099
## SR4 SR2 - SR5 SR2 0.00664 0.0113 13 0.587 0.5672inter_te_ev_1<-glmmTMB(Te_inter~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="Cd"))
inter_te_ev_2<-glmmTMB(Te_inter+1~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="Cd"), family=Gamma(link="log"))
inter_te_ev_3<-glmmTMB(Te_inter+1~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="Cd"), family=gaussian(link="log"))
anova(inter_te_ev_1,inter_te_ev_2,inter_te_ev_3)## Data: subset(param_all_w0, Environment == "Cd")
## Models:
## inter_te_ev_1: Te_inter ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## inter_te_ev_2: Te_inter + 1 ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## inter_te_ev_3: Te_inter + 1 ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_te_ev_1 5 -74.432 -69.980 42.216 -84.432
## inter_te_ev_2 5 -74.123 -69.671 42.061 -84.123 0.000 0 1
## inter_te_ev_3 5 -74.432 -69.980 42.216 -84.432 0.309 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_ev_3)## Family: gaussian ( log )
## Formula: Te_inter + 1 ~ Te_Regime * Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## -74.4 -70.0 42.2 -84.4 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000538
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01309 0.01051 -1.246 0.2127
## Te_RegimeSR5 0.05236 0.01448 3.616 0.0003 ***
## Tu_RegimeSR2 0.01487 0.01563 0.951 0.3415
## Te_RegimeSR5:Tu_RegimeSR2 -0.05149 0.02185 -2.357 0.0184 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_ev_1)## Family: gaussian ( identity )
## Formula: Te_inter ~ Te_Regime * Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## -74.4 -70.0 42.2 -84.4 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000538
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.01300 0.01037 -1.254 0.209753
## Te_RegimeSR5 0.05306 0.01466 3.618 0.000296 ***
## Tu_RegimeSR2 0.01478 0.01555 0.951 0.341857
## Te_RegimeSR5:Tu_RegimeSR2 -0.05218 0.02199 -2.372 0.017672 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairs(emmeans(inter_te_ev_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 -0.053059 0.0147 13 -3.618 0.0031
## SR4 SR1 - SR4 SR2 -0.014783 0.0156 13 -0.951 0.3592
## SR4 SR1 - SR5 SR2 -0.015661 0.0156 13 -1.007 0.3323
## SR5 SR1 - SR4 SR2 0.038276 0.0156 13 2.461 0.0286
## SR5 SR1 - SR5 SR2 0.037398 0.0156 13 2.405 0.0318
## SR4 SR2 - SR5 SR2 -0.000878 0.0164 13 -0.054 0.9581simulationOutput <- simulateResiduals(fittedModel = inter_te_ev_1, plot = F, )
#plot(simulationOutput)
plotResiduals(simulationOutput, subset(param_all_w0, Environment=="Cd")$Te_Regime)## Warning in ensurePredictor(simulationOutput, form): DHARMa:::ensurePredictor:
## character string was provided as predictor. DHARMa has converted to factor
## automatically. To remove this warning, please convert to factor before
## attempting to plot with DHARMa.
plotResiduals(simulationOutput, subset(param_all_w0, Environment=="Cd")$Tu_Regime)## Warning in ensurePredictor(simulationOutput, form): DHARMa:::ensurePredictor:
## character string was provided as predictor. DHARMa has converted to factor
## automatically. To remove this warning, please convert to factor before
## attempting to plot with DHARMa.
# No problemSummary
summary(gr_tu_ev_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -7.8 -7.3 6.9 -13.8 6
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.00744
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.20062 0.03858 5.201 1.98e-07 ***
## Tu_RegimeSR2 0.14393 0.05786 2.487 0.0129 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_ev_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## 12.2 13.1 -3.1 6.2 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0289
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.56092 0.07602 7.379 1.6e-13 ***
## Te_RegimeSR5 0.22763 0.10751 2.117 0.0342 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## intra
summary(intra_tu_ev_1)## Family: gaussian ( identity )
## Formula: Tu_intra ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -43.4 -42.8 24.7 -49.4 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000242
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.009066 0.006956 1.303 0.192
## Tu_RegimeSR2 0.003776 0.010434 0.362 0.717summary(intra_te_ev_1)## Family: gaussian ( identity )
## Formula: Te_intra ~ Te_Regime
## Data: subset(param_all_w0, Environment == "Cd" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -49.0 -48.1 27.5 -55.0 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000238
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.005138 0.006902 0.744 0.45661
## Te_RegimeSR5 0.028576 0.009761 2.928 0.00342 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## inter
pairs(emmeans(inter_tu_ev_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 0.01187 0.0101 13 1.173 0.2617
## SR4 SR1 - SR4 SR2 -0.00390 0.0107 13 -0.364 0.7217
## SR4 SR1 - SR5 SR2 0.00273 0.0107 13 0.255 0.8029
## SR5 SR1 - SR4 SR2 -0.01577 0.0107 13 -1.470 0.1653
## SR5 SR1 - SR5 SR2 -0.00913 0.0107 13 -0.851 0.4099
## SR4 SR2 - SR5 SR2 0.00664 0.0113 13 0.587 0.5672pairs(emmeans(inter_te_ev_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 -0.053059 0.0147 13 -3.618 0.0031
## SR4 SR1 - SR4 SR2 -0.014783 0.0156 13 -0.951 0.3592
## SR4 SR1 - SR5 SR2 -0.015661 0.0156 13 -1.007 0.3323
## SR5 SR1 - SR4 SR2 0.038276 0.0156 13 2.461 0.0286
## SR5 SR1 - SR5 SR2 0.037398 0.0156 13 2.405 0.0318
## SR4 SR2 - SR5 SR2 -0.000878 0.0164 13 -0.054 0.9581# Anova
Anova(gr_tu_ev_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_lambda
## Chisq Df Pr(>Chisq)
## Tu_Regime 6.1871 1 0.01287 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(gr_te_ev_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_lambda
## Chisq Df Pr(>Chisq)
## Te_Regime 4.4834 1 0.03423 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## intra
Anova(intra_tu_ev_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_intra
## Chisq Df Pr(>Chisq)
## Tu_Regime 0.131 1 0.7174Anova(intra_te_ev_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_intra
## Chisq Df Pr(>Chisq)
## Te_Regime 8.57 1 0.003418 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Inter
Anova(inter_tu_ev_1, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Tu_inter
## Chisq Df Pr(>Chisq)
## (Intercept) 2.2835 1 0.1308
## Tu_Regime 0.1325 1 0.7158
## Te_Regime 1.3767 1 0.2407
## Tu_Regime:Te_Regime 0.1188 1 0.7304Anova(inter_te_ev_1, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Te_inter
## Chisq Df Pr(>Chisq)
## (Intercept) 1.5731 1 0.2097531
## Te_Regime 13.0935 1 0.0002963 ***
## Tu_Regime 0.9035 1 0.3418567
## Te_Regime:Tu_Regime 5.6283 1 0.0176724 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 12.3.4 - Does evolution change the ancestral?
gr_tu_an_1<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="N" & Te_Regime=="SR4"))
gr_tu_an_2<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="N"& Te_Regime=="SR4"), family=Gamma(link="log"))
gr_tu_an_3<-glmmTMB(Tu_lambda~Tu_Regime, data=subset(param_all_w0, Environment=="N"& Te_Regime=="SR4"), family=gaussian(link="log"))
anova(gr_tu_an_1,gr_tu_an_2,gr_tu_an_3)## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
## Models:
## gr_tu_an_1: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## gr_tu_an_2: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## gr_tu_an_3: Tu_lambda ~ Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_tu_an_1 3 19.212 19.803 -6.6058 13.212
## gr_tu_an_2 3 18.643 19.235 -6.3215 12.643 0.5685 0 <2e-16 ***
## gr_tu_an_3 3 19.212 19.803 -6.6058 13.212 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_tu_an_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 18.6 19.2 -6.3 12.6 6
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0372
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.92178 0.08625 10.688 <2e-16 ***
## Tu_RegimeSR2 0.04502 0.12937 0.348 0.728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_tu_an_1)## Family: gaussian ( identity )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 19.2 19.8 -6.6 13.2 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.254
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.5138 0.2254 11.150 <2e-16 ***
## Tu_RegimeSR2 0.1158 0.3382 0.342 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_tu_an_2, plot = F, )
plot(simulationOutput)# no problem## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
gr_te_an_1<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"))
gr_te_an_2<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"), family=Gamma(link="log"))
gr_te_an_3<-glmmTMB(Te_lambda~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"), family=gaussian(link="log"))
anova(gr_te_an_1,gr_te_an_2,gr_te_an_3)## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
## Models:
## gr_te_an_1: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## gr_te_an_2: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## gr_te_an_3: Te_lambda ~ Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## gr_te_an_1 3 33.492 34.399 -13.746 27.491
## gr_te_an_2 3 31.700 32.608 -12.850 25.700 1.7918 0 <2e-16 ***
## gr_te_an_3 3 33.492 34.399 -13.746 27.491 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_an_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Te_Regime
## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## 31.7 32.6 -12.8 25.7 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0283
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.74354 0.07529 23.157 <2e-16 ***
## Te_RegimeSR5 -0.17262 0.10648 -1.621 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = gr_te_an_2, plot = F, )
plot(simulationOutput)## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
## intra
intra_tu_an_1<-glmmTMB(Tu_intra~Tu_Regime, data=subset(param_all_w0, Environment=="N"& Te_Regime=="SR4"))
intra_tu_an_2<-glmmTMB(Tu_intra+1~Tu_Regime, data=subset(param_all_w0, Environment=="N"& Te_Regime=="SR4"), family=Gamma(link="log"))
intra_tu_an_3<-glmmTMB(Tu_intra+1~Tu_Regime, data=subset(param_all_w0, Environment=="N"& Te_Regime=="SR4"), family=gaussian(link="log"))
anova(intra_tu_an_1,intra_tu_an_2,intra_tu_an_3)## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
## Models:
## intra_tu_an_1: Tu_intra ~ Tu_Regime, zi=~0, disp=~1
## intra_tu_an_2: Tu_intra + 1 ~ Tu_Regime, zi=~0, disp=~1
## intra_tu_an_3: Tu_intra + 1 ~ Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_tu_an_1 3 -30.949 -30.357 18.474 -36.949
## intra_tu_an_2 3 -30.931 -30.339 18.465 -36.931 0.0000 0 1
## intra_tu_an_3 3 -30.949 -30.357 18.474 -36.949 0.0184 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_an_3)## Family: gaussian ( log )
## Formula: Tu_intra + 1 ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -30.9 -30.4 18.5 -36.9 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000965
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.792e-02 1.351e-02 2.067 0.0388 *
## Tu_RegimeSR2 -6.347e-05 2.027e-02 -0.003 0.9975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_an_1)## Family: gaussian ( identity )
## Formula: Tu_intra ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -30.9 -30.4 18.5 -36.9 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000965
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0283165 0.0138929 2.038 0.0415 *
## Tu_RegimeSR2 -0.0000653 0.0208393 -0.003 0.9975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = intra_tu_an_1, plot = F, )
plot(simulationOutput) #no problem## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
intra_te_an_1<-glmmTMB(Te_intra~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"))
intra_te_an_2<-glmmTMB(Te_intra+1~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"), family=Gamma(link="log"))
intra_te_an_3<-glmmTMB(Te_intra+1~Te_Regime, data=subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1"), family=gaussian(link="log"))
anova(intra_te_an_1,intra_te_an_2,intra_te_an_3)## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
## Models:
## intra_te_an_1: Te_intra ~ Te_Regime, zi=~0, disp=~1
## intra_te_an_2: Te_intra + 1 ~ Te_Regime, zi=~0, disp=~1
## intra_te_an_3: Te_intra + 1 ~ Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## intra_te_an_1 3 -40.712 -39.805 23.356 -46.712
## intra_te_an_2 3 -40.562 -39.654 23.281 -46.562 0.0000 0 1
## intra_te_an_3 3 -40.712 -39.805 23.356 -46.712 0.1508 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_an_3)## Family: gaussian ( log )
## Formula: Te_intra + 1 ~ Te_Regime
## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -40.7 -39.8 23.4 -46.7 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000548
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01862 0.01028 1.812 0.0699 .
## Te_RegimeSR5 0.01732 0.01441 1.202 0.2294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_an_1)## Family: gaussian ( identity )
## Formula: Te_intra ~ Te_Regime
## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -40.7 -39.8 23.4 -46.7 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000548
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01880 0.01047 1.796 0.0726 .
## Te_RegimeSR5 0.01780 0.01481 1.202 0.2294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = intra_te_an_1, plot = F, )
plot(simulationOutput)# no problem## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.25. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.5. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.## Warning in smooth.construct.tp.smooth.spec(object, dk$data, dk$knots): basis dimension, k, increased to minimum possible## Unable to calculate quantile regression for quantile 0.75. Possibly to few (unique) data points / predictions. Will be ommited in plots and significance calculations.
## inter
inter_tu_an_1<-glmmTMB(Tu_inter~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="N"))
inter_tu_an_2<-glmmTMB(Tu_inter+1~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="N"), family=Gamma(link="log"))
inter_tu_an_3<-glmmTMB(Tu_inter+1~Tu_Regime*Te_Regime, data=subset(param_all_w0, Environment=="N"), family=gaussian(link="log"))
anova(inter_tu_an_1,inter_tu_an_2,inter_tu_an_3)## Data: subset(param_all_w0, Environment == "N")
## Models:
## inter_tu_an_1: Tu_inter ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## inter_tu_an_2: Tu_inter + 1 ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## inter_tu_an_3: Tu_inter + 1 ~ Tu_Regime * Te_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_tu_an_1 5 -66.934 -62.482 38.467 -76.934
## inter_tu_an_2 5 -66.587 -62.135 38.293 -76.587 0.0000 0 1
## inter_tu_an_3 5 -66.934 -62.482 38.467 -76.934 0.3473 0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_tu_an_3)## Family: gaussian ( log )
## Formula: Tu_inter + 1 ~ Tu_Regime * Te_Regime
## Data: subset(param_all_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -66.9 -62.5 38.5 -76.9 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000815
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.039664 0.012273 3.232 0.00123 **
## Tu_RegimeSR2 0.002957 0.018379 0.161 0.87218
## Te_RegimeSR5 -0.009884 0.017443 -0.567 0.57093
## Tu_RegimeSR2:Te_RegimeSR5 -0.017747 0.026253 -0.676 0.49903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_tu_an_1)## Family: gaussian ( identity )
## Formula: Tu_inter ~ Tu_Regime * Te_Regime
## Data: subset(param_all_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -66.9 -62.5 38.5 -76.9 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000815
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.040461 0.012769 3.169 0.00153 **
## Tu_RegimeSR2 0.003081 0.019154 0.161 0.87220
## Te_RegimeSR5 -0.010234 0.018058 -0.567 0.57092
## Tu_RegimeSR2:Te_RegimeSR5 -0.018206 0.027088 -0.672 0.50150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1simulationOutput <- simulateResiduals(fittedModel = inter_tu_an_1, plot = F, )
plot(simulationOutput) # no problem
pairs(emmeans(inter_tu_an_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 0.01023 0.0181 13 0.567 0.5806
## SR4 SR1 - SR4 SR2 -0.00308 0.0192 13 -0.161 0.8747
## SR4 SR1 - SR5 SR2 0.02536 0.0192 13 1.324 0.2083
## SR5 SR1 - SR4 SR2 -0.01331 0.0192 13 -0.695 0.4992
## SR5 SR1 - SR5 SR2 0.01513 0.0192 13 0.790 0.4439
## SR4 SR2 - SR5 SR2 0.02844 0.0202 13 1.409 0.1824inter_te_an_1<-glmmTMB(Te_inter~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="N"))
inter_te_an_2<-glmmTMB(Te_inter+1~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="N"), family=Gamma(link="log"))
inter_te_an_3<-glmmTMB(Te_inter+1~Te_Regime*Tu_Regime, data=subset(param_all_w0, Environment=="N"), family=gaussian(link="log"))
anova(inter_te_an_1,inter_te_an_2,inter_te_an_3)## Data: subset(param_all_w0, Environment == "N")
## Models:
## inter_te_an_1: Te_inter ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## inter_te_an_2: Te_inter + 1 ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## inter_te_an_3: Te_inter + 1 ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## inter_te_an_1 5 -35.652 -31.20 22.826 -45.652
## inter_te_an_2 5 -37.001 -32.55 23.501 -47.001 1.3491 0 <2e-16 ***
## inter_te_an_3 5 -35.652 -31.20 22.826 -45.652 0.0000 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_an_2)## Family: Gamma ( log )
## Formula: Te_inter + 1 ~ Te_Regime * Tu_Regime
## Data: subset(param_all_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -37.0 -32.5 23.5 -47.0 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.00363
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.101837 0.026940 3.780 0.000157 ***
## Te_RegimeSR5 0.014480 0.038099 0.380 0.703903
## Tu_RegimeSR2 -0.046878 0.040410 -1.160 0.246022
## Te_RegimeSR5:Tu_RegimeSR2 -0.009634 0.057148 -0.169 0.866126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(inter_te_an_1)## Family: gaussian ( identity )
## Formula: Te_inter ~ Te_Regime * Tu_Regime
## Data: subset(param_all_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -35.7 -31.2 22.8 -45.7 13
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.00464
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10720 0.03045 3.521 0.00043 ***
## Te_RegimeSR5 0.01615 0.04306 0.375 0.70763
## Tu_RegimeSR2 -0.05071 0.04567 -1.110 0.26688
## Te_RegimeSR5:Tu_RegimeSR2 -0.01102 0.06459 -0.171 0.86456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairs(emmeans(inter_te_an_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 -0.01615 0.0431 13 -0.375 0.7137
## SR4 SR1 - SR4 SR2 0.05071 0.0457 13 1.110 0.2870
## SR4 SR1 - SR5 SR2 0.04557 0.0457 13 0.998 0.3365
## SR5 SR1 - SR4 SR2 0.06685 0.0457 13 1.464 0.1670
## SR5 SR1 - SR5 SR2 0.06172 0.0457 13 1.351 0.1996
## SR4 SR2 - SR5 SR2 -0.00513 0.0481 13 -0.107 0.9167simulationOutput <- simulateResiduals(fittedModel = inter_te_an_1, plot = F, )
plot(simulationOutput) # no problems
inter_te_an_1_2<-glmmTMB(Te_inter~Te_Regime+Tu_Regime, data=subset(param_all_w0, Environment=="N"))
summary(inter_te_an_1_2)## Family: gaussian ( identity )
## Formula: Te_inter ~ Te_Regime + Tu_Regime
## Data: subset(param_all_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -37.6 -34.1 22.8 -45.6 14
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.00464
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.10965 0.02687 4.080 4.5e-05 ***
## Te_RegimeSR5 0.01125 0.03212 0.350 0.726
## Tu_RegimeSR2 -0.05621 0.03232 -1.739 0.082 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairs(emmeans(inter_te_an_1_2, pairwise~Te_Regime+Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 -0.0113 0.0321 14 -0.350 0.7313
## SR4 SR1 - SR4 SR2 0.0562 0.0323 14 1.739 0.1039
## SR4 SR1 - SR5 SR2 0.0450 0.0456 14 0.987 0.3405
## SR5 SR1 - SR4 SR2 0.0675 0.0456 14 1.481 0.1609
## SR5 SR1 - SR5 SR2 0.0562 0.0323 14 1.739 0.1039
## SR4 SR2 - SR5 SR2 -0.0113 0.0321 14 -0.350 0.7313Summary
summary(gr_tu_an_2)## Family: Gamma ( log )
## Formula: Tu_lambda ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## 18.6 19.2 -6.3 12.6 6
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0372
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.92178 0.08625 10.688 <2e-16 ***
## Tu_RegimeSR2 0.04502 0.12937 0.348 0.728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(gr_te_an_2)## Family: Gamma ( log )
## Formula: Te_lambda ~ Te_Regime
## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## 31.7 32.6 -12.8 25.7 7
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.0283
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.74354 0.07529 23.157 <2e-16 ***
## Te_RegimeSR5 -0.17262 0.10648 -1.621 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_tu_an_1)## Family: gaussian ( identity )
## Formula: Tu_intra ~ Tu_Regime
## Data: subset(param_all_w0, Environment == "N" & Te_Regime == "SR4")
##
## AIC BIC logLik deviance df.resid
## -30.9 -30.4 18.5 -36.9 6
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000965
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.0283165 0.0138929 2.038 0.0415 *
## Tu_RegimeSR2 -0.0000653 0.0208393 -0.003 0.9975
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(intra_te_an_1)## Family: gaussian ( identity )
## Formula: Te_intra ~ Te_Regime
## Data: subset(param_all_w0, Environment == "N" & Tu_Regime == "SR1")
##
## AIC BIC logLik deviance df.resid
## -40.7 -39.8 23.4 -46.7 7
##
##
## Dispersion estimate for gaussian family (sigma^2): 0.000548
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.01880 0.01047 1.796 0.0726 .
## Te_RegimeSR5 0.01780 0.01481 1.202 0.2294
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairs(emmeans(inter_tu_an_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 0.01023 0.0181 13 0.567 0.5806
## SR4 SR1 - SR4 SR2 -0.00308 0.0192 13 -0.161 0.8747
## SR4 SR1 - SR5 SR2 0.02536 0.0192 13 1.324 0.2083
## SR5 SR1 - SR4 SR2 -0.01331 0.0192 13 -0.695 0.4992
## SR5 SR1 - SR5 SR2 0.01513 0.0192 13 0.790 0.4439
## SR4 SR2 - SR5 SR2 0.02844 0.0202 13 1.409 0.1824pairs(emmeans(inter_te_an_1, pairwise~Te_Regime:Tu_Regime), adjust="none")## contrast estimate SE df t.ratio p.value
## SR4 SR1 - SR5 SR1 -0.01615 0.0431 13 -0.375 0.7137
## SR4 SR1 - SR4 SR2 0.05071 0.0457 13 1.110 0.2870
## SR4 SR1 - SR5 SR2 0.04557 0.0457 13 0.998 0.3365
## SR5 SR1 - SR4 SR2 0.06685 0.0457 13 1.464 0.1670
## SR5 SR1 - SR5 SR2 0.06172 0.0457 13 1.351 0.1996
## SR4 SR2 - SR5 SR2 -0.00513 0.0481 13 -0.107 0.9167Anova(gr_tu_an_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_lambda
## Chisq Df Pr(>Chisq)
## Tu_Regime 0.1211 1 0.7278Anova(gr_te_an_2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_lambda
## Chisq Df Pr(>Chisq)
## Te_Regime 2.6282 1 0.105Anova(intra_tu_an_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Tu_intra
## Chisq Df Pr(>Chisq)
## Tu_Regime 0 1 0.9975Anova(intra_te_an_1)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Te_intra
## Chisq Df Pr(>Chisq)
## Te_Regime 1.4447 1 0.2294Anova(inter_tu_an_1, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Tu_inter
## Chisq Df Pr(>Chisq)
## (Intercept) 10.0403 1 0.001532 **
## Tu_Regime 0.0259 1 0.872203
## Te_Regime 0.3211 1 0.570918
## Tu_Regime:Te_Regime 0.4518 1 0.501504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(inter_te_an_1, type=3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Te_inter
## Chisq Df Pr(>Chisq)
## (Intercept) 12.3975 1 0.0004299 ***
## Te_Regime 0.1407 1 0.7076304
## Tu_Regime 1.2327 1 0.2668838
## Te_Regime:Tu_Regime 0.0291 1 0.8645601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1pairs(emmeans(inter_te_an_1, pairwise~Tu_Regime| Te_Regime), adjust="none")## Te_Regime = SR4:
## contrast estimate SE df t.ratio p.value
## SR1 - SR2 0.0507 0.0457 13 1.110 0.2870
##
## Te_Regime = SR5:
## contrast estimate SE df t.ratio p.value
## SR1 - SR2 0.0617 0.0457 13 1.351 0.19963 - Predicting coexistence (structural stability)
3.1 - Structural stability
Defining functions
The structural stability approach takes a LV, in the appendix we show how to expand to other type of model_w0s mostly a Beverton Holt function, we did not explore the ricker function, but we suspect it will work equally good, So no worries, we are good to go. Regarding the function to calculate structural niche and fitness differences, These are the functions (omega is niche differences) (theta is fitness differences). For calculating theta you also need to calculate the centroid. The function test feasibility is to know whether a species combination can or can not coexist.
#input parameters:
#alpha = competition strenght matrix
#r = vector of intrinsic growth rates
#structural niche difference (output on a log scale)
Omega <- function(alpha){
n <- nrow(alpha)
Sigma <-solve(t(alpha) %*% alpha, tol = 1e-40)
d <- pmvnorm(lower = rep(0,n), upper = rep(Inf,n), mean = rep(0,n), sigma = Sigma)
out <- log10(d[1]) + n * log10(2)
return(out)
}
#vector defining the centroid of the feasibility domain
r_centroid <- function(alpha){
n <- nrow(alpha)
D <- diag(1/sqrt(diag(t(alpha)%*%alpha)))
alpha_n <- alpha %*% D
r_c <- rowSums(alpha_n) /n
r_c <- t(t(r_c))
return(r_c)
}
#structural fitness difference (in degree)
theta <- function(alpha,r){
r_c <- r_centroid(alpha)
out <- acos(sum(r_c*r, na.rm = TRUE)/(sqrt(sum(r^2, na.rm = TRUE))*sqrt(sum(r_c^2, na.rm = TRUE))))*90/pi
return(out)
}
#test if a system (alpha and r) is feasible (output 1 = feasible, 0 = not feasible)
test_feasibility <- function(alpha,r){
out <- prod(solve(alpha,r)>0)
return(out)
}3.1.1 - estimating structural stability for all replicates
#x<-2
struct_mat_REP<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_REP[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_REP$Te_intra[x],param_all_REP$Te_inter[x],param_all_REP$Tu_inter[x], param_all_REP$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_REP$Te_lambda[x],param_all_REP$Tu_lambda[x])
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
colnames(struct_mat_REP)<-c("ND", "FD", "Feasibility")
#For the lower we use the higher alphas with lower lambda, and for upper the other way around
# Since we have facilitation we have to actually test what is the lowest value
struct_mat_REP_U<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_REP_lower[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_REP_upper$Te_intra[x],param_all_REP_upper$Te_inter[x], param_all_REP_upper$Tu_inter[x],param_all_REP_upper$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_REP_upper$Te_lambda[x],param_all_REP_upper$Tu_lambda[x] )
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
struct_mat_REP_L<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_REP_upper[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_REP_lower$Te_intra[x],param_all_REP_lower$Te_inter[x],param_all_REP_lower$Tu_inter[x],param_all_REP_lower$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_REP_lower$Te_lambda[x],param_all_REP_lower$Tu_lambda[x] )
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
colnames(struct_mat_REP_U)<-c("ND_U", "FD_U", "Feasibility_U")
colnames(struct_mat_REP_L)<-c("ND_L", "FD_L", "Feasibility_L")
struct_mat_REP<-cbind(param_all_REP, struct_mat_REP,struct_mat_REP_L,struct_mat_REP_U)
# To create the boundaries
bound_struct_rk_w0<-data.frame(ND=seq(0,1, 0.01))
bound_struct_rk_w0$FD<-45*bound_struct_rk_w0$ND
struct_mat_REP3<-struct_mat_REP3.1.2 - estimating structural stability per replicate
struct_mat_w0<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_w0[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_w0$Te_intra[x],param_all_w0$Te_inter[x],param_all_w0$Tu_inter[x], param_all_w0$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_w0$Te_lambda[x],param_all_w0$Tu_lambda[x] )
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
colnames(struct_mat_w0)<-c("ND", "FD", "Feasibility")
struct_mat_w0_L<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_w0_lower[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_w0_upper$Te_intra[x],param_all_w0_upper$Te_inter[x],param_all_w0_upper$Tu_inter[x], param_all_w0_upper$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_w0_lower$Te_lambda[x],param_all_w0_lower$Tu_lambda[x] )
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
colnames(struct_mat_w0_L)<-c("ND_L", "FD_L", "Feasibility_L")
struct_mat_w0_U<-as.data.frame(t(as.data.frame(sapply(c(1:length(param_all_w0_upper[,1])), function(x){
#print(x)
aux_alpha<-matrix(c(param_all_w0_lower$Te_intra[x],param_all_w0_lower$Te_inter[x],param_all_w0_lower$Tu_inter[x], param_all_w0_lower$Tu_intra[x]), ncol=2, byrow=TRUE)
aux_lambda<-c(param_all_w0_upper$Te_lambda[x],param_all_w0_upper$Tu_lambda[x] )
om<-Omega(aux_alpha)
tta<-theta(aux_alpha, aux_lambda)
feas<- test_feasibility(aux_alpha, aux_lambda)
c(om, tta, feas)
}))))
colnames(struct_mat_w0_U)<-c("ND_U", "FD_U", "Feasibility_U")
struct_mat_w0<-cbind(param_all_w0, struct_mat_w0,struct_mat_w0_L,struct_mat_w0_U)
bound_struct_rk_w0<-data.frame(ND=seq(0,1, 0.01))
bound_struct_rk_w0$FD<-45*bound_struct_rk_w0$ND3.2 - Distance to the edge and who wins
struct_mat_REP$a21_a11<-struct_mat_REP$Te_inter/struct_mat_REP$Tu_intra
struct_mat_REP$a22_a12<-struct_mat_REP$Te_intra/struct_mat_REP$Tu_inter
## Now to calculate the upper and lower bounds we have to see which is the lowest value of competition (and those create the upper bounds) or the highest values of competition (those create the lower boundaries)
struct_mat_REP$a21_a11_upper<-param_all_REP_lower$Te_inter/param_all_REP_lower$Tu_intra
struct_mat_REP$a22_a12_upper<-param_all_REP_lower$Te_intra/param_all_REP_lower$Tu_inter
struct_mat_REP$a21_a11_lower<-param_all_REP_upper$Te_inter/param_all_REP_upper$Tu_intra
struct_mat_REP$a22_a12_lower<-param_all_REP_upper$Te_intra/param_all_REP_upper$Tu_inter
struct_mat_REP$Tu_lambda_lower<-param_all_REP_lower$Tu_lambda
struct_mat_REP$Te_lambda_lower<-param_all_REP_lower$Te_lambda
struct_mat_REP$Tu_lambda_upper<-param_all_REP_upper$Tu_lambda
struct_mat_REP$Te_lambda_upper<-param_all_REP_upper$Te_lambdaFinal figure arrows
struct_mat_REP$min_a21_a11<-sapply(c(1:dim(struct_mat_REP)[1]), function(x){
min(c(struct_mat_REP$a21_a11_lower[x],struct_mat_REP$a21_a11_upper[x]))})
struct_mat_REP$min_a22_a12<-sapply(c(1:dim(struct_mat_REP)[1]), function(x){
min(c(struct_mat_REP$a22_a12_lower[x],struct_mat_REP$a22_a12_upper[x]))})
struct_mat_REP$max_a21_a11<-sapply(c(1:dim(struct_mat_REP)[1]), function(x){
max(c(struct_mat_REP$a21_a11_lower[x],struct_mat_REP$a21_a11_upper[x]))})
struct_mat_REP$max_a22_a12<-sapply(c(1:dim(struct_mat_REP)[1]), function(x){
max(c(struct_mat_REP$a22_a12_lower[x],struct_mat_REP$a22_a12_upper[x]))})
#write.csv(struct_mat_REP, "Analyses/structural_REP.csv")per replicate
struct_mat_w0$a21_a11<-struct_mat_w0$Te_inter/struct_mat_w0$Tu_intra
struct_mat_w0$a22_a12<-struct_mat_w0$Te_intra/struct_mat_w0$Tu_inter
struct_mat_w0$a21_a11_lower<-param_all_w0_lower$Te_inter/param_all_w0_lower$Tu_intra
struct_mat_w0$a22_a12_lower<-param_all_w0_lower$Te_intra/param_all_w0_lower$Tu_inter
struct_mat_w0$a21_a11_upper<-param_all_w0_upper$Te_inter/param_all_w0_upper$Tu_intra
struct_mat_w0$a22_a12_upper<-param_all_w0_upper$Te_intra/param_all_w0_upper$Tu_inter
#write.csv(struct_mat_w0, "Analyses/structural_REP_w0.csv")Estimating distance to the edge
Importing to prevent problems with reading information
struct_mat_REP_final<-read.csv("./Analyses/structural_REP.csv")
struct_mat_REP_final<-struct_mat_REP_final[,-1]
struct_mat_w0<-read.csv("./Analyses/structural_REP_w0.csv")
struct_mat_w0<-struct_mat_w0[,-1]Testing difference to one of the edges of the cone
all replicates
# calculating y for the x corresponding to the lambda Tu, in the vector slope
struct_mat_REP$Tu_lambda[1]*struct_mat_REP$a21_a11[1]## [1] 7.247462# just to be sure, it is equivalent to use the Te or Tu lambda
acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda[1], struct_mat_REP$Te_lambda[1]))
,LSAfun::normalize(c(struct_mat_REP$Tu_lambda[1],struct_mat_REP$Tu_lambda[1]*struct_mat_REP$a21_a11[1]))))## [1] 0.09134927acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda[1], struct_mat_REP$Te_lambda[1]))
,LSAfun::normalize(c(struct_mat_REP$Te_lambda[1]/struct_mat_REP$a21_a11[1], struct_mat_REP$Te_lambda[1]))))## [1] 0.09134927#And if I use a random value of 10
acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda[1], struct_mat_REP$Te_lambda[1])),
LSAfun::normalize(c(10,10*struct_mat_REP$a21_a11[1]))))## [1] 0.09134927# also ok, so I can just use the lambda's to do this
struct_mat_REP$distanceTu<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda[x], struct_mat_REP$Te_lambda[x]))
,LSAfun::normalize(c(struct_mat_REP$Tu_lambda[x],struct_mat_REP$Tu_lambda[x]*struct_mat_REP$a21_a11[x])))))
struct_mat_REP$distanceTe<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda[x], struct_mat_REP$Te_lambda[x]))
,LSAfun::normalize(c(struct_mat_REP$Te_lambda[x]/struct_mat_REP$a22_a12[x],struct_mat_REP$Te_lambda[x])))))
struct_mat_REP$distanceTu_lower<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda_lower[x], struct_mat_REP$Te_lambda_lower[x]))
,LSAfun::normalize(c(struct_mat_REP$Tu_lambda_lower[x],struct_mat_REP$Tu_lambda_lower[x]*struct_mat_REP$a21_a11_lower[x])))))
struct_mat_REP$distanceTu_upper<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda_upper[x], struct_mat_REP$Te_lambda_upper[x]))
,LSAfun::normalize(c(struct_mat_REP$Tu_lambda_upper[x],struct_mat_REP$Tu_lambda_upper[x]*struct_mat_REP$a21_a11_upper[x])))))
struct_mat_REP$distanceTe_lower<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda_lower[x], struct_mat_REP$Te_lambda_lower[x]))
,LSAfun::normalize(c(struct_mat_REP$Te_lambda_lower[x]/struct_mat_REP$a22_a12_lower[x],struct_mat_REP$Te_lambda_lower[x])))))
struct_mat_REP$distanceTe_upper<-sapply(c(1:length(struct_mat_REP$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_REP$Tu_lambda_upper[x], struct_mat_REP$Te_lambda_upper[x]))
,LSAfun::normalize(c(struct_mat_REP$Te_lambda_upper[x]/struct_mat_REP$a22_a12_upper[x],struct_mat_REP$Te_lambda_upper[x])))))per replicate
# calculating y for the x corresponding to the lambda Tu, in the vector slope
struct_mat_w0$Tu_lambda[1]*struct_mat_w0$a21_a11[1]## [1] 5.059375# just to be sure, it is equivalent to use the Te or Tu lambda
acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda[1], struct_mat_w0$Te_lambda[1]))
,LSAfun::normalize(c(struct_mat_w0$Tu_lambda[1],struct_mat_w0$Tu_lambda[1]*struct_mat_w0$a21_a11[1]))))## [1] 0.01325358acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda[1], struct_mat_w0$Te_lambda[1]))
,LSAfun::normalize(c(struct_mat_w0$Te_lambda[1]/struct_mat_w0$a21_a11[1], struct_mat_w0$Te_lambda[1]))))## [1] 0.01325358#And if I use a random value of 10
acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda[1], struct_mat_w0$Te_lambda[1])),
LSAfun::normalize(c(10,10*struct_mat_w0$a21_a11[1]))))## [1] 0.01325358# also ok, so I can just use the lambda's to do this
struct_mat_w0$distanceTu<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda[x], struct_mat_w0$Te_lambda[x]))
,LSAfun::normalize(c(struct_mat_w0$Tu_lambda[x],struct_mat_w0$Tu_lambda[x]*struct_mat_w0$a21_a11[x])))))
struct_mat_w0$distanceTe<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda[x], struct_mat_w0$Te_lambda[x]))
,LSAfun::normalize(c(struct_mat_w0$Te_lambda[x]/struct_mat_w0$a22_a12[x],struct_mat_w0$Te_lambda[x])))))
struct_mat_w0$distanceTu_lower<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda_lower[x], struct_mat_w0$Te_lambda_lower[x]))
,LSAfun::normalize(c(struct_mat_w0$Tu_lambda_lower[x],struct_mat_w0$Tu_lambda_lower[x]*struct_mat_w0$a21_a11_lower[x])))))
struct_mat_w0$distanceTu_upper<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda_upper[x], struct_mat_w0$Te_lambda_upper[x]))
,LSAfun::normalize(c(struct_mat_w0$Tu_lambda_upper[x],struct_mat_w0$Tu_lambda_upper[x]*struct_mat_w0$a21_a11_upper[x])))))
struct_mat_w0$distanceTe_lower<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda_lower[x], struct_mat_w0$Te_lambda_lower[x]))
,LSAfun::normalize(c(struct_mat_w0$Te_lambda_lower[x]/struct_mat_w0$a22_a12_lower[x],struct_mat_w0$Te_lambda_lower[x])))))
struct_mat_w0$distanceTe_upper<-sapply(c(1:length(struct_mat_w0$ND)), function(x) acos(Dot(LSAfun::normalize(c(struct_mat_w0$Tu_lambda_upper[x], struct_mat_w0$Te_lambda_upper[x]))
,LSAfun::normalize(c(struct_mat_w0$Te_lambda_upper[x]/struct_mat_w0$a22_a12_upper[x],struct_mat_w0$Te_lambda_upper[x])))))
# Putting distance as negative if the system is unfeasible (i.e. if there is no coexistence, because then they are outside of the feasibility cone)
struct_mat_w0$distanceTu2<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility[x]==0)
a<-struct_mat_w0$distanceTu[x]*-1
else
a<-struct_mat_w0$distanceTu[x]
a
})
struct_mat_w0$distanceTe2<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility[x]==0)
a<-struct_mat_w0$distanceTe[x]*-1
else
a<-struct_mat_w0$distanceTe[x]
a
})
struct_mat_REP$distanceTu2<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility[x]==0)
a<-struct_mat_REP$distanceTu[x]*-1
else
a<-struct_mat_REP$distanceTu[x]
a
})
struct_mat_REP$distanceTe2<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility[x]==0)
a<-struct_mat_REP$distanceTe[x]*-1
else
a<-struct_mat_REP$distanceTe[x]
a
})
#lower and upper bounds
struct_mat_w0$distanceTu2_lower<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility_L[x]==0)
a<-struct_mat_w0$distanceTu_lower[x]*-1
else
a<-struct_mat_w0$distanceTu_lower[x]
a
})
struct_mat_w0$distanceTe2_lower<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility_L[x]==0)
a<-struct_mat_w0$distanceTe_lower[x]*-1
else
a<-struct_mat_w0$distanceTe_lower[x]
a
})
struct_mat_w0$distanceTu2_upper<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility_L[x]==0)
a<-struct_mat_w0$distanceTu_upper[x]*-1
else
a<-struct_mat_w0$distanceTu_upper[x]
a
})
struct_mat_w0$distanceTe2_upper<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
if(struct_mat_w0$Feasibility_L[x]==0)
a<-struct_mat_w0$distanceTe_upper[x]*-1
else
a<-struct_mat_w0$distanceTe_upper[x]
a
})
struct_mat_REP$distanceTu2_upper<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility_U[x]==0)
a<-struct_mat_REP$distanceTu_upper[x]*-1
else
a<-struct_mat_REP$distanceTu_upper[x]
a
})
struct_mat_REP$distanceTe2_upper<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility_U[x]==0)
a<-struct_mat_REP$distanceTe_upper[x]*-1
else
a<-struct_mat_REP$distanceTe_upper[x]
a
})
struct_mat_REP$distanceTu2_lower<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility_U[x]==0)
a<-struct_mat_REP$distanceTu_lower[x]*-1
else
a<-struct_mat_REP$distanceTu_lower[x]
a
})
struct_mat_REP$distanceTe2_lower<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
if(struct_mat_REP$Feasibility_U[x]==0)
a<-struct_mat_REP$distanceTe_lower[x]*-1
else
a<-struct_mat_REP$distanceTe_lower[x]
a
})per replicate
struct_mat_w0$minDistance<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
min( c(struct_mat_w0$distanceTu[x], struct_mat_w0$distanceTe[x]))
})
struct_mat_w0$minDistance2<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
a<-min( c(abs(struct_mat_w0$distanceTu2[x]), abs(struct_mat_w0$distanceTe2[x])))
if(struct_mat_w0$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})
struct_mat_REP$minDistance2<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
a<-min( c(abs(struct_mat_REP$distanceTu2[x]), abs(struct_mat_REP$distanceTe2[x])))
if(struct_mat_REP$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})checking which is the shortest distance
#struct_mat_REP<-struct_mat_REP[,-19]
struct_mat_REP$minDistance<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
min( c(struct_mat_REP$distanceTu[x], struct_mat_REP$distanceTe[x]))
})
struct_mat_w0$minDistance<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
min( c(struct_mat_w0$distanceTu[x], struct_mat_w0$distanceTe[x]))
})
struct_mat_REP$minDistance_L<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
a<-colnames(struct_mat_REP[x,which(struct_mat_REP[x,]==min( c(struct_mat_REP$distanceTu[x], struct_mat_REP$distanceTe[x])))])[1]
if(a=="distanceTu"){
res<- struct_mat_REP$distanceTu_lower[x]
}else if(a=="distanceTe"){
res<- struct_mat_REP$distanceTe_lower[x]
}else
res<-NA
res
})
struct_mat_REP$minDistance_U<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
a<-colnames(struct_mat_REP[x,which(struct_mat_REP[x,]==min( c(struct_mat_REP$distanceTu[x], struct_mat_REP$distanceTe[x])))])[1]
if(a=="distanceTu"){
res<- struct_mat_REP$distanceTu_upper[x]
}else if(a=="distanceTe"){
res<- struct_mat_REP$distanceTe_upper[x]
}else
res<-NA
res
})
struct_mat_w0$minDistance_L<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
a<-colnames(struct_mat_w0[x,which(struct_mat_w0[x,]==min( c(struct_mat_w0$distanceTu[x], struct_mat_w0$distanceTe[x])))])[1]
if(a=="distanceTu"){
res<- struct_mat_w0$distanceTu_lower[x]
}else if(a=="distanceTe"){
res<- struct_mat_w0$distanceTe_lower[x]
}else
res<-NA
res
})
struct_mat_w0$minDistance_U<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
a<-colnames(struct_mat_w0[x,which(struct_mat_w0[x,]==min( c(struct_mat_w0$distanceTu[x], struct_mat_w0$distanceTe[x])))])[1]
if(a=="distanceTu"){
res<- struct_mat_w0$distanceTu_upper[x]
}else if(a=="distanceTe"){
res<- struct_mat_w0$distanceTe_upper[x]
}else
res<-NA
res
})
struct_mat_w0$minDistance2_lower<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
a<-struct_mat_w0$minDistance_L[x]
if(struct_mat_w0$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})
struct_mat_REP$minDistance2_lower<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
a<-struct_mat_REP$minDistance_L[x]
if(struct_mat_REP$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})
struct_mat_w0$minDistance2_upper<-sapply(c(1:length(struct_mat_w0$ND)), function(x){
a<-struct_mat_w0$minDistance_U[x]
if(struct_mat_w0$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})
struct_mat_REP$minDistance2_upper<-sapply(c(1:length(struct_mat_REP$ND)), function(x){
a<-struct_mat_REP$minDistance_U[x]
if(struct_mat_REP$Feasibility[x]==0)
a2<-a*-1
else
a2<-a
})na.omit(subset(struct_mat_w0, Environment=="Cd"))## Tu_Regime Te_Regime Replicate Environment Tu_lambda Te_lambda Tu_intra
## 19 SR1 SR4 1 Cd 1.263518 2.079319 0.008968545
## 20 SR2 SR4 1 Cd 1.559691 2.079319 0.040797476
## 21 SR1 SR5 1 Cd 1.263518 2.685230 0.008968545
## 22 SR2 SR5 1 Cd 1.559691 2.685230 0.040797476
## 23 SR1 SR4 2 Cd 1.098786 1.363013 -0.000853826
## 24 SR1 SR5 2 Cd 1.098786 1.612001 -0.000853826
## 25 SR1 SR4 3 Cd 1.155278 1.980124 0.009196018
## 26 SR2 SR4 3 Cd 1.481804 1.980124 0.024641295
## 27 SR1 SR5 3 Cd 1.155278 1.876019 0.009196018
## 28 SR2 SR5 3 Cd 1.481804 1.876019 0.024641295
## 29 SR1 SR4 4 Cd 1.200719 1.597399 0.005190474
## 30 SR2 SR4 4 Cd 1.217856 1.597399 0.002009553
## 31 SR1 SR5 4 Cd 1.200719 2.287674 0.005190474
## 32 SR2 SR5 4 Cd 1.217856 2.287674 0.002009553
## 33 SR1 SR4 5 Cd 1.392517 1.741602 0.022828169
## 34 SR2 SR4 5 Cd 1.386070 1.741602 -0.016080607
## 35 SR1 SR5 5 Cd 1.392517 2.540195 0.022828169
## 36 SR2 SR5 5 Cd 1.386070 2.540195 -0.016080607
## Te_intra Tu_inter Te_inter ND FD Feasibility
## 19 0.021943476 0.0255938201 -0.011320658 0.01059047 32.108281 0
## 20 0.021943476 0.0185628236 0.022957187 -0.64452913 6.778830 0
## 21 0.049727551 0.0161563696 0.040176078 -1.22064484 4.953829 0
## 22 0.049727551 0.0256111879 0.019650450 -0.38566557 7.808030 1
## 23 -0.010072024 -0.0035455089 -0.040243208 -0.69471029 75.715150 0
## 24 0.008329916 -0.0004215226 0.045156365 -1.69568614 18.134770 0
## 25 0.007206893 -0.0075988794 0.051910250 -0.20171454 24.248113 0
## 26 0.007206893 0.0277985348 -0.029389759 -0.14430138 35.467599 0
## 27 0.027942371 -0.0078557435 0.045986919 -0.52269958 16.911296 0
## 28 0.027942371 0.0257896361 -0.003616994 -0.20881742 16.112063 0
## 29 -0.013887492 0.0061943962 -0.055841136 -0.68174129 64.197115 0
## 30 -0.013887492 -0.0043296513 0.010301717 0.28561317 56.572495 0
## 31 0.030862480 0.0139779146 0.054868584 -0.67635311 6.404134 0
## 32 0.030862480 -0.0209888470 -0.006842631 0.25566864 71.660734 0
## 33 0.020501123 0.0333857080 -0.009528609 -0.22017331 23.453553 0
## 34 0.020501123 0.0168103044 0.003244074 0.11743723 29.068331 1
## 35 0.051710287 -0.0271574132 0.014084317 -0.01957060 6.710900 1
## 36 0.051710287 0.0018787191 0.001434420 -0.01480644 35.015123 0
## ND_L FD_L Feasibility_L ND_U FD_U Feasibility_U
## 19 -1.54462751 3.990633 0 0.04721106 53.030793 0
## 20 -2.11727214 2.439802 0 0.27834857 4.277922 1
## 21 -2.21394499 3.418906 0 -0.49340581 14.243236 0
## 22 -0.73610998 3.632260 1 0.02906109 17.316621 1
## 23 -0.02775893 31.187148 0 -0.53245541 82.830862 0
## 24 -1.18449002 13.455195 0 0.15741639 58.614527 0
## 25 -1.02687854 4.736395 0 0.09822193 46.312483 0
## 26 -0.32451735 17.008548 0 -0.45456203 58.163677 0
## 27 -1.12245717 7.389838 0 -0.14014526 34.895549 0
## 28 -0.60125687 4.455240 1 0.03285158 35.275185 0
## 29 -0.04707808 38.215311 0 -1.07420487 75.541593 0
## 30 -0.38062249 2.533284 1 -0.78537030 89.968911 0
## 31 -0.74671232 5.442735 0 -1.34364125 16.447221 0
## 32 -0.49162415 4.587629 1 -0.05262855 75.348837 0
## 33 -0.69289429 10.207231 0 -0.06234705 43.508446 0
## 34 -0.30644174 8.552267 1 -0.24996960 70.524428 0
## 35 -0.31990808 6.283201 1 0.22110741 80.832302 0
## 36 -0.68826698 10.413547 0 0.02909675 55.579969 0
## a21_a11 a22_a12 a21_a11_lower a22_a12_lower a21_a11_upper
## 19 -1.26226253 0.8573740 9.0857011 0.07088079 1.10634567
## 20 0.56271097 1.1821195 -0.4252340 -0.62964225 1.07273088
## 21 4.47966524 3.0778914 -2.9048510 5.72984898 2.78937851
## 22 0.48165849 1.9416339 -0.4048642 3.53091672 0.93931981
## 23 47.13279520 2.8407836 6.0199143 1.38435740 -1.47044720
## 24 -52.88707823 -19.7614947 -2.5520915 1.78307147 6.61844676
## 25 5.64486195 -0.9484152 -8.9792716 0.55876803 3.25601432
## 26 -1.19270348 0.2592544 -4.4518500 -1.02618862 -0.07114558
## 27 5.00074282 -3.5569352 -9.4888928 -0.34055590 2.63386539
## 28 -0.14678586 1.0834729 -2.2200104 0.60851475 0.56666514
## 29 -10.75838893 -2.2419445 17.8416083 5.24495164 -2.13868952
## 30 5.12637107 3.2075314 1.0340694 1.78287601 2.20746540
## 31 10.57101640 2.2079459 -6.5835829 -12.54561134 5.40082396
## 32 -3.40505060 -1.4704228 3.3946318 -0.10762180 1.44494169
## 33 -0.41740574 0.6140688 -3.5738589 0.06285109 0.39340973
## 34 -0.20173830 1.2195569 0.7980935 -0.21294191 568.11732111
## 35 0.61697093 -1.9040947 -1.8336006 -0.60412494 1.24646271
## 36 -0.08920183 27.5242242 0.8960476 -1.39547379 559.94105303
## a22_a12_upper distanceTu distanceTe distanceTu_lower distanceTu_upper
## 19 1.2113052 1.9255763 0.31600455 1.570796 1.570796
## 20 1.0988574 0.4146835 0.05856812 1.570796 1.570796
## 21 2.7072981 0.2201760 0.12566655 1.570796 1.570796
## 22 1.7135250 0.5957167 0.05061334 1.570796 1.570796
## 23 0.5462526 0.6572633 0.34000744 1.570796 1.570796
## 24 3.8731053 2.5243970 0.64885008 1.570796 1.570796
## 25 2.0934537 0.3528186 1.34001902 1.570796 1.570796
## 26 0.7971894 1.8014160 0.67468953 1.570796 1.570796
## 27 4.0103058 0.3545989 0.82603227 1.570796 1.570796
## 28 1.2836487 1.0480102 0.07682405 1.570796 1.570796
## 29 0.2832304 2.4043384 1.06412693 1.570796 1.570796
## 30 0.5353995 0.4587392 0.34917339 1.570796 1.570796
## 31 1.9955580 0.3890231 0.05806978 1.570796 1.570796
## 32 10.0699110 2.3667470 1.08642471 1.570796 1.570796
## 33 0.8310639 1.2917438 0.34562306 1.570796 1.570796
## 34 1.0160601 1.0976517 0.01458862 1.570796 1.570796
## 35 -13.1558712 0.5165368 0.98504597 1.570796 1.570796
## 36 2.9797918 1.1602615 0.46318556 1.570796 1.570796
## distanceTe_lower distanceTe_upper distanceTu2 distanceTe2 distanceTu2_lower
## 19 1.570796 1.570796 -1.9255763 -0.31600455 -1.570796
## 20 1.570796 1.570796 -0.4146835 -0.05856812 -1.570796
## 21 1.570796 1.570796 -0.2201760 -0.12566655 -1.570796
## 22 1.570796 1.570796 0.5957167 0.05061334 1.570796
## 23 1.570796 1.570796 -0.6572633 -0.34000744 -1.570796
## 24 1.570796 1.570796 -2.5243970 -0.64885008 -1.570796
## 25 1.570796 1.570796 -0.3528186 -1.34001902 -1.570796
## 26 1.570796 1.570796 -1.8014160 -0.67468953 -1.570796
## 27 1.570796 1.570796 -0.3545989 -0.82603227 -1.570796
## 28 1.570796 1.570796 -1.0480102 -0.07682405 1.570796
## 29 1.570796 1.570796 -2.4043384 -1.06412693 -1.570796
## 30 1.570796 1.570796 -0.4587392 -0.34917339 1.570796
## 31 1.570796 1.570796 -0.3890231 -0.05806978 -1.570796
## 32 1.570796 1.570796 -2.3667470 -1.08642471 1.570796
## 33 1.570796 1.570796 -1.2917438 -0.34562306 -1.570796
## 34 1.570796 1.570796 1.0976517 0.01458862 1.570796
## 35 1.570796 1.570796 0.5165368 0.98504597 1.570796
## 36 1.570796 1.570796 -1.1602615 -0.46318556 -1.570796
## distanceTe2_lower distanceTu2_upper distanceTe2_upper minDistance
## 19 -1.570796 -1.570796 -1.570796 0.31600455
## 20 -1.570796 -1.570796 -1.570796 0.05856812
## 21 -1.570796 -1.570796 -1.570796 0.12566655
## 22 1.570796 1.570796 1.570796 0.05061334
## 23 -1.570796 -1.570796 -1.570796 0.34000744
## 24 -1.570796 -1.570796 -1.570796 0.64885008
## 25 -1.570796 -1.570796 -1.570796 0.35281858
## 26 -1.570796 -1.570796 -1.570796 0.67468953
## 27 -1.570796 -1.570796 -1.570796 0.35459893
## 28 1.570796 1.570796 1.570796 0.07682405
## 29 -1.570796 -1.570796 -1.570796 1.06412693
## 30 1.570796 1.570796 1.570796 0.34917339
## 31 -1.570796 -1.570796 -1.570796 0.05806978
## 32 1.570796 1.570796 1.570796 1.08642471
## 33 -1.570796 -1.570796 -1.570796 0.34562306
## 34 1.570796 1.570796 1.570796 0.01458862
## 35 1.570796 1.570796 1.570796 0.51653680
## 36 -1.570796 -1.570796 -1.570796 0.46318556
## minDistance2 minDistance_L minDistance_U minDistance2_lower
## 19 -0.31600455 1.570796 1.570796 -1.570796
## 20 -0.05856812 1.570796 1.570796 -1.570796
## 21 -0.12566655 1.570796 1.570796 -1.570796
## 22 0.05061334 1.570796 1.570796 1.570796
## 23 -0.34000744 1.570796 1.570796 -1.570796
## 24 -0.64885008 1.570796 1.570796 -1.570796
## 25 -0.35281858 1.570796 1.570796 -1.570796
## 26 -0.67468953 1.570796 1.570796 -1.570796
## 27 -0.35459893 1.570796 1.570796 -1.570796
## 28 -0.07682405 1.570796 1.570796 -1.570796
## 29 -1.06412693 1.570796 1.570796 -1.570796
## 30 -0.34917339 1.570796 1.570796 -1.570796
## 31 -0.05806978 1.570796 1.570796 -1.570796
## 32 -1.08642471 1.570796 1.570796 -1.570796
## 33 -0.34562306 1.570796 1.570796 -1.570796
## 34 0.01458862 1.570796 1.570796 1.570796
## 35 0.51653680 1.570796 1.570796 1.570796
## 36 -0.46318556 1.570796 1.570796 -1.570796
## minDistance2_upper
## 19 -1.570796
## 20 -1.570796
## 21 -1.570796
## 22 1.570796
## 23 -1.570796
## 24 -1.570796
## 25 -1.570796
## 26 -1.570796
## 27 -1.570796
## 28 -1.570796
## 29 -1.570796
## 30 -1.570796
## 31 -1.570796
## 32 -1.570796
## 33 -1.570796
## 34 1.570796
## 35 1.570796
## 36 -1.570796Stats
# descdist(subset(struct_mat_w0, Environment=="Cd")$distanceTu, discrete = FALSE, boot = 500)
# descdist(subset(struct_mat_w0, Environment=="Cd")$distanceTe , discrete = FALSE, boot = 500)
#
# descdist(subset(struct_mat_w0, Environment=="N")$distanceTu , discrete = FALSE, boot = 500)
# descdist(subset(struct_mat_w0, Environment=="N")$distanceTe , discrete = FALSE, boot = 500)
dist_Cd_Tu<-glmmTMB(distanceTu~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=Gamma(link="log"))
dist_Cd_Tu2<-glmmTMB(distanceTu~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=Gamma(link="identity"))
dist_Cd_Tu3<-glmmTMB(distanceTu~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=gaussian(link="log"))
#dist_Cd_Tu4<-glmmTMB(distanceTu~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=beta_family(link="log"))
dist_Cd_Te<-glmmTMB(distanceTe~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=Gamma(link="log"))
dist_Cd_Te2<-glmmTMB(distanceTe~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=Gamma(link="identity"))## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluation
## Warning in (function (start, objective, gradient = NULL, hessian = NULL, :
## NA/NaN function evaluationdist_Cd_Te3<-glmmTMB(distanceTe~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=gaussian(link="log"))
#dist_Cd_Te4<-glmmTMB(distanceTe~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=beta_family(link="logit"))
anova(dist_Cd_Tu, dist_Cd_Tu2, dist_Cd_Tu3)## Data: subset(struct_mat_w0, Environment == "Cd")
## Models:
## dist_Cd_Tu: distanceTu ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## dist_Cd_Tu2: distanceTu ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## dist_Cd_Tu3: distanceTu ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dist_Cd_Tu 5 43.079 47.531 -16.539 33.079
## dist_Cd_Tu2 5 43.079 47.531 -16.539 33.079 0 0 <2e-16 ***
## dist_Cd_Tu3 5 49.891 54.343 -19.945 39.891 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(dist_Cd_Te, dist_Cd_Te2, dist_Cd_Te3)## Data: subset(struct_mat_w0, Environment == "Cd")
## Models:
## dist_Cd_Te: distanceTe ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## dist_Cd_Te2: distanceTe ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## dist_Cd_Te3: distanceTe ~ Te_Regime * Tu_Regime, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dist_Cd_Te 5 18.41 22.861 -4.2047 8.4095
## dist_Cd_Te2 5 18.41 22.861 -4.2047 8.4095 0 0 <2e-16 ***
## dist_Cd_Te3 5 26.38 30.832 -8.1899 16.3799 0 0 1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(dist_Cd_Tu)## Family: Gamma ( log )
## Formula: distanceTu ~ Te_Regime * Tu_Regime
## Data: subset(struct_mat_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## 43.1 47.5 -16.5 33.1 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.458
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2824 0.3028 0.933 0.351
## Te_RegimeSR5 -0.5044 0.4282 -1.178 0.239
## Tu_RegimeSR2 -0.3410 0.4542 -0.751 0.453
## Te_RegimeSR5:Tu_RegimeSR2 0.8197 0.6424 1.276 0.202summary(dist_Cd_Te)## Family: Gamma ( log )
## Formula: distanceTe ~ Te_Regime * Tu_Regime
## Data: subset(struct_mat_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## 18.4 22.9 -4.2 8.4 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.922
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3840 0.4293 -0.894 0.371
## Te_RegimeSR5 -0.2533 0.6072 -0.417 0.677
## Tu_RegimeSR2 -0.9097 0.6440 -1.413 0.158
## Te_RegimeSR5:Tu_RegimeSR2 0.6777 0.9108 0.744 0.457dist_N<-glmmTMB(minDistance~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="N"), family=Gamma(link="log"))
dist_Cd<-glmmTMB(minDistance~Te_Regime*Tu_Regime, data=subset(struct_mat_w0, Environment=="Cd"), family=Gamma(link="log"))
summary(dist_Cd)## Family: Gamma ( log )
## Formula: minDistance ~ Te_Regime * Tu_Regime
## Data: subset(struct_mat_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## 10.3 14.7 -0.1 0.3 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.826
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7263 0.4065 -1.787 0.074 .
## Te_RegimeSR5 -0.3504 0.5749 -0.610 0.542
## Tu_RegimeSR2 -0.5674 0.6097 -0.931 0.352
## Te_RegimeSR5:Tu_RegimeSR2 0.7748 0.8623 0.898 0.369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(dist_Cd)## Family: Gamma ( log )
## Formula: minDistance ~ Te_Regime * Tu_Regime
## Data: subset(struct_mat_w0, Environment == "Cd")
##
## AIC BIC logLik deviance df.resid
## 10.3 14.7 -0.1 0.3 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.826
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7263 0.4065 -1.787 0.074 .
## Te_RegimeSR5 -0.3504 0.5749 -0.610 0.542
## Tu_RegimeSR2 -0.5674 0.6097 -0.931 0.352
## Te_RegimeSR5:Tu_RegimeSR2 0.7748 0.8623 0.898 0.369
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(dist_N)## Family: Gamma ( log )
## Formula: minDistance ~ Te_Regime * Tu_Regime
## Data: subset(struct_mat_w0, Environment == "N")
##
## AIC BIC logLik deviance df.resid
## -6.8 -2.4 8.4 -16.8 13
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.535
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4440 0.3272 -4.413 1.02e-05 ***
## Te_RegimeSR5 0.2572 0.4628 0.556 0.578
## Tu_RegimeSR2 0.1428 0.4908 0.291 0.771
## Te_RegimeSR5:Tu_RegimeSR2 -0.5336 0.6942 -0.769 0.442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dist_glm<-glmmTMB(minDistance~Te_Regime*Tu_Regime*Environment, data=struct_mat_w0, family=Gamma(link="log"))
summary(dist_glm)## Family: Gamma ( log )
## Formula: minDistance ~ Te_Regime * Tu_Regime * Environment
## Data: struct_mat_w0
##
## AIC BIC logLik deviance df.resid
## 2.5 16.7 7.8 -15.5 27
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.683
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7263 0.3696 -1.965 0.0494 *
## Te_RegimeSR5 -0.3504 0.5227 -0.670 0.5027
## Tu_RegimeSR2 -0.5674 0.5544 -1.023 0.3061
## EnvironmentN -0.7178 0.5227 -1.373 0.1697
## Te_RegimeSR5:Tu_RegimeSR2 0.7748 0.7841 0.988 0.3231
## Te_RegimeSR5:EnvironmentN 0.6076 0.7393 0.822 0.4112
## Tu_RegimeSR2:EnvironmentN 0.7102 0.7841 0.906 0.3650
## Te_RegimeSR5:Tu_RegimeSR2:EnvironmentN -1.3084 1.1089 -1.180 0.2380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dist_glm2<-glmmTMB(minDistance~Te_Regime+Tu_Regime+Environment, data=struct_mat_w0, family=Gamma(link="log"))
dist_glm3<-glmmTMB(minDistance~Te_Regime*Environment+Tu_Regime*Environment, data=struct_mat_w0, family=Gamma(link="log"))
summary(dist_glm3)## Family: Gamma ( log )
## Formula:
## minDistance ~ Te_Regime * Environment + Tu_Regime * Environment
## Data: struct_mat_w0
##
## AIC BIC logLik deviance df.resid
## -0.1 11.0 7.1 -14.1 29
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.706
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.883241 0.313953 -2.813 0.0049 **
## Te_RegimeSR5 -0.007078 0.403306 -0.018 0.9860
## EnvironmentN -0.435413 0.467933 -0.930 0.3521
## Tu_RegimeSR2 -0.171602 0.405819 -0.423 0.6724
## Te_RegimeSR5:EnvironmentN 0.027484 0.567633 0.048 0.9614
## EnvironmentN:Tu_RegimeSR2 0.051585 0.571169 0.090 0.9280
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(dist_glm2)## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: minDistance
## Chisq Df Pr(>Chisq)
## Te_Regime 0.0001 1 0.9926
## Tu_Regime 0.2752 1 0.5999
## Environment 2.0271 1 0.1545anova(dist_glm, dist_glm2, dist_glm3)## Data: struct_mat_w0
## Models:
## dist_glm2: minDistance ~ Te_Regime + Tu_Regime + Environment, zi=~0, disp=~1
## dist_glm3: minDistance ~ Te_Regime * Environment + Tu_Regime * Environment, zi=~0, disp=~1
## dist_glm: minDistance ~ Te_Regime * Tu_Regime * Environment, zi=~0, disp=~1
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## dist_glm2 5 -4.1109 3.8067 7.0554 -14.111
## dist_glm3 7 -0.1216 10.9630 7.0608 -14.122 0.0108 2 0.9946
## dist_glm 9 2.4695 16.7212 7.7652 -15.530 1.4088 2 0.4944dist_Cd_Te<-glmmTMB(distanceTe~Environment, data=struct_mat_w0, family=Gamma(link="log"))
summary(dist_Cd_Te)## Family: Gamma ( log )
## Formula: distanceTe ~ Environment
## Data: struct_mat_w0
##
## AIC BIC logLik deviance df.resid
## 25.2 30.0 -9.6 19.2 33
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.662
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.71295 0.19173 -3.719 0.0002 ***
## EnvironmentN 0.05222 0.27115 0.193 0.8473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1dist_Cd_Tu<-glmmTMB(distanceTu~Environment, data=struct_mat_w0, family=Gamma(link="log"))
summary(dist_Cd_Tu)## Family: Gamma ( log )
## Formula: distanceTu ~ Environment
## Data: struct_mat_w0
##
## AIC BIC logLik deviance df.resid
## 75.6 80.4 -34.8 69.6 33
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.962
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.08412 0.23122 0.364 0.716
## EnvironmentN -0.23322 0.32700 -0.713 0.476dist_Cd<-glmmTMB(minDistance~Environment, data=struct_mat_w0, family=Gamma(link="log"))
summary(dist_Cd)## Family: Gamma ( log )
## Formula: minDistance ~ Environment
## Data: struct_mat_w0
##
## AIC BIC logLik deviance df.resid
## -7.8 -3.1 6.9 -13.8 33
##
##
## Dispersion estimate for Gamma family (sigma^2): 0.71
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.9594 0.1986 -4.830 1.36e-06 ***
## EnvironmentN -0.4005 0.2809 -1.426 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14 - Can we predict the outcome of species interactions?
str(sum_coex_g42_res2)## 'data.frame': 1440 obs. of 10 variables:
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 1 1 1 1 1 ...
## $ Box2 : Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 2 2 ...
## $ Leaf2 : chr "2" "2" "3" "3" ...
## $ X1st.pair: Factor w/ 8 levels "2.4","24c","2c4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ X2nd.pair: Factor w/ 10 levels "1.4","2.4","24c",..: 5 5 5 5 5 5 5 5 5 5 ...
## $ Env : chr "Cd" "water" "Cd" "water" ...
## $ av_Te : int 85 0 90 0 18 6 206 116 9 2 ...
## $ av_Tu : int 11 1 2 1 0 7 22 33 0 3 ...coex_no_het<-subset(sum_coex_g42_res2, Env!="Heterogeneous")
coex_no_het2<-coex_no_het %>%
group_by(SRTu, SRTe, Rep2, Box2, Env) %>%
summarize(sumTe=sum(av_Te, na.rm=TRUE), sumTu=sum(av_Tu, na.rm=TRUE)) %>% as.data.frame()## `summarise()` has grouped output by 'SRTu', 'SRTe', 'Rep2', 'Box2'. You can
## override using the `.groups` argument.coex_no_het2$Te_ratio<-coex_no_het2$sumTe/(coex_no_het2$sumTe+coex_no_het2$sumTu)
coex_no_het2$Te_ratio[which(coex_no_het2$Te_ratio=="NaN")]<-0
coex_no_het2$Tu_Regime<-mapvalues(coex_no_het2$SRTu, c("Tu2", "Tu1"), c("SR2", "SR1"))
coex_no_het2$Te_Regime<-mapvalues(coex_no_het2$SRTe, c("Te4", "Te5"), c("SR4", "SR5"))Predicting once
pred_coex_RK_REP<-expand_grid(Te=c("SR4","SR5"), Tu=c("SR1", "SR2"), Environment= c("N", "Cd"))
pred_coex_RK_REP$predTu1<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6 - aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_REP$predTe1<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_REP$predTu2<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*pred_coex_RK_REP$predTu1[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_REP$predTu1[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_REP$predTe1[x])
bl
})
pred_coex_RK_REP$predTe2<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*pred_coex_RK_REP$predTe1[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_REP$predTe1[x] - aux_alphas$Te_inter[1]*pred_coex_RK_REP$predTu1[x])
bl
})
x<-1
# lower - stronger alpha and lower lambda
pred_coex_RK_REP$predTu1_L<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6- aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_REP$predTe1_L<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_REP$predTu2_L<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Tu_lambda[1]*pred_coex_RK_REP$predTu1_L[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_REP$predTu1_L[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_REP$predTe1_L[x])
bl
})
pred_coex_RK_REP$predTe2_L<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Te_lambda[1]*pred_coex_RK_REP$predTe1_L[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_REP$predTe1_L[x] - aux_alphas$Te_inter[1]*pred_coex_RK_REP$predTu1_L[x])
bl
})
# upper
pred_coex_RK_REP$predTu1_U<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6- aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_REP$predTe1_U<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_REP$predTu2_U<-sapply(c(1:length(pred_coex_RK_REP$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Tu_lambda[1]* pred_coex_RK_REP$predTu1_U[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_REP$predTu1_U[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_REP$predTe1_U[x])
bl
})
pred_coex_RK_REP$predTe2_U<-sapply(c(1:length(pred_coex_RK_REP$Te)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
aux_lambda<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex_RK_REP$Tu[x]) & Te_Regime==as.character(pred_coex_RK_REP$Te[x]) & Environment==as.character(pred_coex_RK_REP$Environment[x]))
bl<-aux_lambda$Te_lambda[1]*pred_coex_RK_REP$predTe1_U[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_REP$predTe1_U[x] - aux_alphas$Te_inter[1]*pred_coex_RK_REP$predTu1_U[x])
bl
})
names(pred_coex_RK_REP)[1:3]<-c("SRTe", "SRTu", "Env")
pred_coex_RK_REP<-as.data.frame(pred_coex_RK_REP)Predicting per replicate
pred_coex_RK_w0<-as.data.frame(expand_grid(Te=c("SR4","SR5"), Tu=c("SR1", "SR2"), Environment= c("N", "Cd"), Replicate=c(1,2,3,4,5)))
pred_coex_RK_w0<- pred_coex_RK_w0[- which(pred_coex_RK_w0$Replicate==2 & pred_coex_RK_w0$Tu=="SR2" & pred_coex_RK_w0$Environment=="Cd"),]
pred_coex_RK_w0$predTu1<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_alphas$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6 - aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_w0$predTe1<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_alphas$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_w0$predTu2<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_alphas$Tu_lambda[1]*pred_coex_RK_w0$predTu1[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_w0$predTu1[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_w0$predTe1[x])
bl
})
pred_coex_RK_w0$predTe2<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_alphas$Te_lambda[1]*pred_coex_RK_w0$predTe1[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_w0$predTe1[x] - aux_alphas$Te_inter[1]*pred_coex_RK_w0$predTu1[x])
bl
})
x<-1
# lower - stronger alpha and lower lambda
pred_coex_RK_w0$predTu1_L<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6- aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_w0$predTe1_L<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_w0$predTu2_L<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Tu_lambda[1]*pred_coex_RK_w0$predTu1_L[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_w0$predTu1_L[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_w0$predTe1_L[x])
bl
})
pred_coex_RK_w0$predTe2_L<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Te_lambda[1]*pred_coex_RK_w0$predTe1_L[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_w0$predTe1_L[x] - aux_alphas$Te_inter[1]*pred_coex_RK_w0$predTu1_L[x])
bl
})
# upper
pred_coex_RK_w0$predTu1_U<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Tu_lambda[1]*6* exp(-aux_alphas$Tu_intra[1]*6- aux_alphas$Tu_inter[1]*6)
bl
})
pred_coex_RK_w0$predTe1_U<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Te_lambda[1]*6* exp(-aux_alphas$Te_intra[1]*6 - aux_alphas$Te_inter[1]*6)
bl
})
pred_coex_RK_w0$predTu2_U<-sapply(c(1:length(pred_coex_RK_w0$Tu)), function(x){
aux_alphas<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Tu_lambda[1]* pred_coex_RK_w0$predTu1_U[x]* exp(-aux_alphas$Tu_intra[1]*pred_coex_RK_w0$predTu1_U[x]- aux_alphas$Tu_inter[1]*pred_coex_RK_w0$predTe1_U[x])
bl
})
pred_coex_RK_w0$predTe2_U<-sapply(c(1:length(pred_coex_RK_w0$Te)), function(x){
aux_alphas<-subset(param_all_w0_lower, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
aux_lambda<-subset(param_all_w0_upper, Tu_Regime==as.character(pred_coex_RK_w0$Tu[x]) & Te_Regime==as.character(pred_coex_RK_w0$Te[x]) & Environment==as.character(pred_coex_RK_w0$Environment[x]) & Replicate==pred_coex_RK_w0$Replicate[x])
bl<-aux_lambda$Te_lambda[1]*pred_coex_RK_w0$predTe1_U[x]* exp(-aux_alphas$Te_intra[1]*pred_coex_RK_w0$predTe1_U[x] - aux_alphas$Te_inter[1]*pred_coex_RK_w0$predTu1_U[x])
bl
})
names(pred_coex_RK_w0)[1:3]<-c("SRTe", "SRTu", "Env")
pred_coex_RK_w0<-as.data.frame(pred_coex_RK_w0)Empirical
coex_g42_rep## Rep2 Leaf2 SRTu SRTe Env Box2 sum_Te sum_Tu
## 1 1 2 Tu1 Te4 Cd 1 85 11
## 2 1 2 Tu1 Te4 Cd 2 9 0
## 3 1 2 Tu1 Te4 Cd 3 0 0
## 4 1 2 Tu1 Te4 Cd 4 66 0
## 5 1 2 Tu1 Te4 Cd 5 35 0
## 6 1 2 Tu1 Te4 Cd 6 164 28
## 7 1 2 Tu1 Te4 Cd 7 29 2
## 8 1 2 Tu1 Te4 Cd 8 48 2
## 9 1 2 Tu1 Te4 Cd 9 0 0
## 10 1 2 Tu1 Te4 Cd 10 22 0
## 11 1 2 Tu1 Te4 water 1 0 1
## 12 1 2 Tu1 Te4 water 2 2 3
## 13 1 2 Tu1 Te4 water 3 18 8
## 14 1 2 Tu1 Te4 water 4 30 9
## 15 1 2 Tu1 Te4 water 5 7 8
## 16 1 2 Tu1 Te4 water 6 23 8
## 17 1 2 Tu1 Te4 water 7 35 30
## 18 1 2 Tu1 Te4 water 8 18 35
## 19 1 2 Tu1 Te4 water 9 19 12
## 20 1 2 Tu1 Te4 water 10 28 77
## 21 1 2 Tu1 Te5 Cd 1 7 4
## 22 1 2 Tu1 Te5 Cd 2 39 2
## 23 1 2 Tu1 Te5 Cd 3 31 2
## 24 1 2 Tu1 Te5 Cd 4 7 0
## 25 1 2 Tu1 Te5 Cd 5 89 2
## 26 1 2 Tu1 Te5 Cd 6 48 0
## 27 1 2 Tu1 Te5 Cd 7 18 0
## 28 1 2 Tu1 Te5 Cd 8 51 0
## 29 1 2 Tu1 Te5 Cd 9 11 0
## 30 1 2 Tu1 Te5 Cd 10 21 0
## 31 1 2 Tu1 Te5 water 1 0 0
## 32 1 2 Tu1 Te5 water 2 0 0
## 33 1 2 Tu1 Te5 water 3 0 0
## 34 1 2 Tu1 Te5 water 4 0 1
## 35 1 2 Tu1 Te5 water 5 0 0
## 36 1 2 Tu1 Te5 water 6 39 4
## 37 1 2 Tu1 Te5 water 7 85 5
## 38 1 2 Tu1 Te5 water 8 34 39
## 39 1 2 Tu1 Te5 water 9 52 16
## 40 1 2 Tu1 Te5 water 10 2 2
## 41 1 2 Tu2 Te4 Cd 1 134 19
## 42 1 2 Tu2 Te4 Cd 2 6 0
## 43 1 2 Tu2 Te4 Cd 3 6 1
## 44 1 2 Tu2 Te4 Cd 4 0 0
## 45 1 2 Tu2 Te4 Cd 5 0 0
## 46 1 2 Tu2 Te4 Cd 6 7 1
## 47 1 2 Tu2 Te4 Cd 7 12 0
## 48 1 2 Tu2 Te4 Cd 8 1 0
## 49 1 2 Tu2 Te4 Cd 9 9 0
## 50 1 2 Tu2 Te4 Cd 10 5 0
## 51 1 2 Tu2 Te4 water 1 0 0
## 52 1 2 Tu2 Te4 water 2 0 0
## 53 1 2 Tu2 Te4 water 3 0 0
## 54 1 2 Tu2 Te4 water 4 0 0
## 55 1 2 Tu2 Te4 water 5 0 0
## 56 1 2 Tu2 Te4 water 6 0 3
## 57 1 2 Tu2 Te4 water 7 31 8
## 58 1 2 Tu2 Te4 water 8 50 3
## 59 1 2 Tu2 Te4 water 9 94 1
## 60 1 2 Tu2 Te4 water 10 62 1
## 61 1 2 Tu2 Te5 Cd 1 39 7
## 62 1 2 Tu2 Te5 Cd 2 40 2
## 63 1 2 Tu2 Te5 Cd 3 2 0
## 64 1 2 Tu2 Te5 Cd 4 19 0
## 65 1 2 Tu2 Te5 Cd 5 14 11
## 66 1 2 Tu2 Te5 Cd 6 8 0
## 67 1 2 Tu2 Te5 Cd 7 0 0
## 68 1 2 Tu2 Te5 Cd 8 2 2
## 69 1 2 Tu2 Te5 Cd 9 1 1
## 70 1 2 Tu2 Te5 Cd 10 16 0
## 71 1 2 Tu2 Te5 water 1 1 1
## 72 1 2 Tu2 Te5 water 2 0 0
## 73 1 2 Tu2 Te5 water 3 0 0
## 74 1 2 Tu2 Te5 water 4 0 0
## 75 1 2 Tu2 Te5 water 5 0 0
## 76 1 2 Tu2 Te5 water 6 48 0
## 77 1 2 Tu2 Te5 water 7 111 40
## 78 1 2 Tu2 Te5 water 8 0 0
## 79 1 2 Tu2 Te5 water 9 191 41
## 80 1 2 Tu2 Te5 water 10 3 0
## 81 1 3 Tu1 Te4 Cd 1 90 2
## 82 1 3 Tu1 Te4 Cd 2 0 0
## 83 1 3 Tu1 Te4 Cd 3 0 0
## 84 1 3 Tu1 Te4 Cd 4 0 0
## 85 1 3 Tu1 Te4 Cd 5 12 2
## 86 1 3 Tu1 Te4 Cd 6 55 12
## 87 1 3 Tu1 Te4 Cd 7 20 0
## 88 1 3 Tu1 Te4 Cd 8 13 0
## 89 1 3 Tu1 Te4 Cd 9 18 0
## 90 1 3 Tu1 Te4 Cd 10 5 0
## 91 1 3 Tu1 Te4 water 1 0 1
## 92 1 3 Tu1 Te4 water 2 9 3
## 93 1 3 Tu1 Te4 water 3 6 1
## 94 1 3 Tu1 Te4 water 4 8 8
## 95 1 3 Tu1 Te4 water 5 3 6
## 96 1 3 Tu1 Te4 water 6 0 0
## 97 1 3 Tu1 Te4 water 7 19 6
## 98 1 3 Tu1 Te4 water 8 0 0
## 99 1 3 Tu1 Te4 water 9 0 0
## 100 1 3 Tu1 Te4 water 10 0 1
## 101 1 3 Tu1 Te5 Cd 1 32 1
## 102 1 3 Tu1 Te5 Cd 2 44 0
## 103 1 3 Tu1 Te5 Cd 3 11 0
## 104 1 3 Tu1 Te5 Cd 4 30 1
## 105 1 3 Tu1 Te5 Cd 5 34 0
## 106 1 3 Tu1 Te5 Cd 6 0 0
## 107 1 3 Tu1 Te5 Cd 7 29 1
## 108 1 3 Tu1 Te5 Cd 8 31 0
## 109 1 3 Tu1 Te5 Cd 9 35 0
## 110 1 3 Tu1 Te5 Cd 10 0 0
## 111 1 3 Tu1 Te5 water 1 0 0
## 112 1 3 Tu1 Te5 water 2 0 0
## 113 1 3 Tu1 Te5 water 3 0 0
## 114 1 3 Tu1 Te5 water 4 0 0
## 115 1 3 Tu1 Te5 water 5 0 0
## 116 1 3 Tu1 Te5 water 6 29 8
## 117 1 3 Tu1 Te5 water 7 36 9
## 118 1 3 Tu1 Te5 water 8 25 28
## 119 1 3 Tu1 Te5 water 9 16 46
## 120 1 3 Tu1 Te5 water 10 3 0
## 121 1 3 Tu2 Te4 Cd 1 1 0
## 122 1 3 Tu2 Te4 Cd 2 0 0
## 123 1 3 Tu2 Te4 Cd 3 17 2
## 124 1 3 Tu2 Te4 Cd 4 9 2
## 125 1 3 Tu2 Te4 Cd 5 0 0
## 126 1 3 Tu2 Te4 Cd 6 8 0
## 127 1 3 Tu2 Te4 Cd 7 0 0
## 128 1 3 Tu2 Te4 Cd 8 0 0
## 129 1 3 Tu2 Te4 Cd 9 0 0
## 130 1 3 Tu2 Te4 Cd 10 1 0
## 131 1 3 Tu2 Te4 water 1 0 0
## 132 1 3 Tu2 Te4 water 2 0 0
## 133 1 3 Tu2 Te4 water 3 0 0
## 134 1 3 Tu2 Te4 water 4 3 2
## 135 1 3 Tu2 Te4 water 5 0 0
## 136 1 3 Tu2 Te4 water 6 21 19
## 137 1 3 Tu2 Te4 water 7 53 23
## 138 1 3 Tu2 Te4 water 8 28 31
## 139 1 3 Tu2 Te4 water 9 30 1
## 140 1 3 Tu2 Te4 water 10 16 4
## 141 1 3 Tu2 Te5 Cd 1 48 10
## 142 1 3 Tu2 Te5 Cd 2 10 0
## 143 1 3 Tu2 Te5 Cd 3 14 1
## 144 1 3 Tu2 Te5 Cd 4 33 6
## 145 1 3 Tu2 Te5 Cd 5 19 2
## 146 1 3 Tu2 Te5 Cd 6 13 0
## 147 1 3 Tu2 Te5 Cd 7 8 0
## 148 1 3 Tu2 Te5 Cd 8 8 1
## 149 1 3 Tu2 Te5 Cd 9 4 0
## 150 1 3 Tu2 Te5 Cd 10 18 0
## 151 1 3 Tu2 Te5 water 1 0 0
## 152 1 3 Tu2 Te5 water 2 0 0
## 153 1 3 Tu2 Te5 water 3 0 0
## 154 1 3 Tu2 Te5 water 4 0 0
## 155 1 3 Tu2 Te5 water 5 0 0
## 156 1 3 Tu2 Te5 water 6 0 0
## 157 1 3 Tu2 Te5 water 7 1 2
## 158 1 3 Tu2 Te5 water 8 0 0
## 159 1 3 Tu2 Te5 water 9 18 4
## 160 1 3 Tu2 Te5 water 10 0 0
## 161 1 4 Tu1 Te4 Cd 1 18 0
## 162 1 4 Tu1 Te4 Cd 2 0 0
## 163 1 4 Tu1 Te4 Cd 3 0 0
## 164 1 4 Tu1 Te4 Cd 4 0 0
## 165 1 4 Tu1 Te4 Cd 5 0 2
## 166 1 4 Tu1 Te4 Cd 6 0 0
## 167 1 4 Tu1 Te4 Cd 7 91 2
## 168 1 4 Tu1 Te4 Cd 8 14 0
## 169 1 4 Tu1 Te4 Cd 9 22 0
## 170 1 4 Tu1 Te4 Cd 10 45 0
## 171 1 4 Tu1 Te4 water 1 6 7
## 172 1 4 Tu1 Te4 water 2 1 2
## 173 1 4 Tu1 Te4 water 3 174 45
## 174 1 4 Tu1 Te4 water 4 4 0
## 175 1 4 Tu1 Te4 water 5 2 1
## 176 1 4 Tu1 Te4 water 6 5 5
## 177 1 4 Tu1 Te4 water 7 32 58
## 178 1 4 Tu1 Te4 water 8 31 42
## 179 1 4 Tu1 Te4 water 9 18 70
## 180 1 4 Tu1 Te4 water 10 24 71
## 181 1 4 Tu1 Te5 Cd 1 22 0
## 182 1 4 Tu1 Te5 Cd 2 0 0
## 183 1 4 Tu1 Te5 Cd 3 17 1
## 184 1 4 Tu1 Te5 Cd 4 0 0
## 185 1 4 Tu1 Te5 Cd 5 3 0
## 186 1 4 Tu1 Te5 Cd 6 1 0
## 187 1 4 Tu1 Te5 Cd 7 55 0
## 188 1 4 Tu1 Te5 Cd 8 20 0
## 189 1 4 Tu1 Te5 Cd 9 0 0
## 190 1 4 Tu1 Te5 Cd 10 34 0
## 191 1 4 Tu1 Te5 water 1 0 0
## 192 1 4 Tu1 Te5 water 2 0 0
## 193 1 4 Tu1 Te5 water 3 1 0
## 194 1 4 Tu1 Te5 water 4 0 0
## 195 1 4 Tu1 Te5 water 5 0 0
## 196 1 4 Tu1 Te5 water 6 81 19
## 197 1 4 Tu1 Te5 water 7 52 11
## 198 1 4 Tu1 Te5 water 8 46 26
## 199 1 4 Tu1 Te5 water 9 22 3
## 200 1 4 Tu1 Te5 water 10 10 1
## 201 1 4 Tu2 Te4 Cd 1 39 6
## 202 1 4 Tu2 Te4 Cd 2 0 0
## 203 1 4 Tu2 Te4 Cd 3 7 5
## 204 1 4 Tu2 Te4 Cd 4 10 3
## 205 1 4 Tu2 Te4 Cd 5 0 0
## 206 1 4 Tu2 Te4 Cd 6 35 0
## 207 1 4 Tu2 Te4 Cd 7 0 0
## 208 1 4 Tu2 Te4 Cd 8 10 0
## 209 1 4 Tu2 Te4 Cd 9 32 0
## 210 1 4 Tu2 Te4 Cd 10 10 0
## 211 1 4 Tu2 Te4 water 1 0 0
## 212 1 4 Tu2 Te4 water 2 0 0
## 213 1 4 Tu2 Te4 water 3 0 0
## 214 1 4 Tu2 Te4 water 4 9 26
## 215 1 4 Tu2 Te4 water 5 0 0
## 216 1 4 Tu2 Te4 water 6 1 1
## 217 1 4 Tu2 Te4 water 7 0 0
## 218 1 4 Tu2 Te4 water 8 119 24
## 219 1 4 Tu2 Te4 water 9 137 0
## 220 1 4 Tu2 Te4 water 10 0 0
## 221 1 4 Tu2 Te5 Cd 1 0 0
## 222 1 4 Tu2 Te5 Cd 2 0 0
## 223 1 4 Tu2 Te5 Cd 3 9 0
## 224 1 4 Tu2 Te5 Cd 4 0 0
## 225 1 4 Tu2 Te5 Cd 5 0 0
## 226 1 4 Tu2 Te5 Cd 6 0 0
## 227 1 4 Tu2 Te5 Cd 7 0 0
## 228 1 4 Tu2 Te5 Cd 8 16 2
## 229 1 4 Tu2 Te5 Cd 9 1 0
## 230 1 4 Tu2 Te5 Cd 10 50 11
## 231 1 4 Tu2 Te5 water 1 0 0
## 232 1 4 Tu2 Te5 water 2 0 0
## 233 1 4 Tu2 Te5 water 3 0 0
## 234 1 4 Tu2 Te5 water 4 0 0
## 235 1 4 Tu2 Te5 water 5 0 0
## 236 1 4 Tu2 Te5 water 6 0 0
## 237 1 4 Tu2 Te5 water 7 63 9
## 238 1 4 Tu2 Te5 water 8 2 0
## 239 1 4 Tu2 Te5 water 9 2 0
## 240 1 4 Tu2 Te5 water 10 5 0
## 241 1 5 Tu1 Te4 Cd 1 206 22
## 242 1 5 Tu1 Te4 Cd 2 0 0
## 243 1 5 Tu1 Te4 Cd 3 61 7
## 244 1 5 Tu1 Te4 Cd 4 30 0
## 245 1 5 Tu1 Te4 Cd 5 21 0
## 246 1 5 Tu1 Te4 Cd 6 48 2
## 247 1 5 Tu1 Te4 Cd 7 8 1
## 248 1 5 Tu1 Te4 Cd 8 65 0
## 249 1 5 Tu1 Te4 Cd 9 17 0
## 250 1 5 Tu1 Te4 Cd 10 119 0
## 251 1 5 Tu1 Te4 water 1 116 33
## 252 1 5 Tu1 Te4 water 2 28 9
## 253 1 5 Tu1 Te4 water 3 24 6
## 254 1 5 Tu1 Te4 water 4 160 98
## 255 1 5 Tu1 Te4 water 5 17 25
## 256 1 5 Tu1 Te4 water 6 38 40
## 257 1 5 Tu1 Te4 water 7 103 37
## 258 1 5 Tu1 Te4 water 8 182 48
## 259 1 5 Tu1 Te4 water 9 278 58
## 260 1 5 Tu1 Te4 water 10 106 37
## 261 1 5 Tu1 Te5 Cd 1 42 2
## 262 1 5 Tu1 Te5 Cd 2 57 3
## 263 1 5 Tu1 Te5 Cd 3 57 7
## 264 1 5 Tu1 Te5 Cd 4 188 20
## 265 1 5 Tu1 Te5 Cd 5 61 2
## 266 1 5 Tu1 Te5 Cd 6 164 1
## 267 1 5 Tu1 Te5 Cd 7 30 0
## 268 1 5 Tu1 Te5 Cd 8 28 0
## 269 1 5 Tu1 Te5 Cd 9 101 0
## 270 1 5 Tu1 Te5 Cd 10 162 0
## 271 1 5 Tu1 Te5 water 1 0 0
## 272 1 5 Tu1 Te5 water 2 0 0
## 273 1 5 Tu1 Te5 water 3 0 0
## 274 1 5 Tu1 Te5 water 4 0 0
## 275 1 5 Tu1 Te5 water 5 0 0
## 276 1 5 Tu1 Te5 water 6 69 34
## 277 1 5 Tu1 Te5 water 7 160 76
## 278 1 5 Tu1 Te5 water 8 53 60
## 279 1 5 Tu1 Te5 water 9 137 102
## 280 1 5 Tu1 Te5 water 10 221 162
## 281 1 5 Tu2 Te4 Cd 1 96 6
## 282 1 5 Tu2 Te4 Cd 2 4 0
## 283 1 5 Tu2 Te4 Cd 3 35 35
## 284 1 5 Tu2 Te4 Cd 4 10 2
## 285 1 5 Tu2 Te4 Cd 5 0 0
## 286 1 5 Tu2 Te4 Cd 6 24 2
## 287 1 5 Tu2 Te4 Cd 7 24 0
## 288 1 5 Tu2 Te4 Cd 8 39 0
## 289 1 5 Tu2 Te4 Cd 9 7 0
## 290 1 5 Tu2 Te4 Cd 10 0 0
## 291 1 5 Tu2 Te4 water 1 0 0
## 292 1 5 Tu2 Te4 water 2 0 0
## 293 1 5 Tu2 Te4 water 3 0 0
## 294 1 5 Tu2 Te4 water 4 2 1
## 295 1 5 Tu2 Te4 water 5 0 0
## 296 1 5 Tu2 Te4 water 6 67 49
## 297 1 5 Tu2 Te4 water 7 49 49
## 298 1 5 Tu2 Te4 water 8 105 53
## 299 1 5 Tu2 Te4 water 9 125 25
## 300 1 5 Tu2 Te4 water 10 158 86
## 301 1 5 Tu2 Te5 Cd 1 104 53
## 302 1 5 Tu2 Te5 Cd 2 122 9
## 303 1 5 Tu2 Te5 Cd 3 11 5
## 304 1 5 Tu2 Te5 Cd 4 42 13
## 305 1 5 Tu2 Te5 Cd 5 46 86
## 306 1 5 Tu2 Te5 Cd 6 26 2
## 307 1 5 Tu2 Te5 Cd 7 18 1
## 308 1 5 Tu2 Te5 Cd 8 92 7
## 309 1 5 Tu2 Te5 Cd 9 45 12
## 310 1 5 Tu2 Te5 Cd 10 58 4
## 311 1 5 Tu2 Te5 water 1 0 0
## 312 1 5 Tu2 Te5 water 2 0 0
## 313 1 5 Tu2 Te5 water 3 0 0
## 314 1 5 Tu2 Te5 water 4 0 0
## 315 1 5 Tu2 Te5 water 5 0 0
## 316 1 5 Tu2 Te5 water 6 18 2
## 317 1 5 Tu2 Te5 water 7 74 4
## 318 1 5 Tu2 Te5 water 8 1 2
## 319 1 5 Tu2 Te5 water 9 327 11
## 320 1 5 Tu2 Te5 water 10 13 1
## 321 2 2 Tu1 Te4 Cd 1 23 0
## 322 2 2 Tu1 Te4 Cd 2 16 0
## 323 2 2 Tu1 Te4 Cd 3 18 0
## 324 2 2 Tu1 Te4 Cd 4 0 0
## 325 2 2 Tu1 Te4 Cd 5 1 0
## 326 2 2 Tu1 Te4 Cd 6 33 0
## 327 2 2 Tu1 Te4 Cd 7 80 0
## 328 2 2 Tu1 Te4 Cd 8 20 0
## 329 2 2 Tu1 Te4 Cd 9 0 0
## 330 2 2 Tu1 Te4 Cd 10 0 0
## 331 2 2 Tu1 Te4 water 1 0 0
## 332 2 2 Tu1 Te4 water 2 0 0
## 333 2 2 Tu1 Te4 water 3 89 90
## 334 2 2 Tu1 Te4 water 4 78 58
## 335 2 2 Tu1 Te4 water 5 0 0
## 336 2 2 Tu1 Te4 water 6 0 0
## 337 2 2 Tu1 Te4 water 7 1 1
## 338 2 2 Tu1 Te4 water 8 28 23
## 339 2 2 Tu1 Te4 water 9 15 21
## 340 2 2 Tu1 Te4 water 10 26 7
## 341 2 2 Tu1 Te5 Cd 1 56 7
## 342 2 2 Tu1 Te5 Cd 2 29 0
## 343 2 2 Tu1 Te5 Cd 3 146 7
## 344 2 2 Tu1 Te5 Cd 4 2 0
## 345 2 2 Tu1 Te5 Cd 5 4 13
## 346 2 2 Tu1 Te5 Cd 6 3 0
## 347 2 2 Tu1 Te5 Cd 7 19 0
## 348 2 2 Tu1 Te5 Cd 8 31 1
## 349 2 2 Tu1 Te5 Cd 9 8 0
## 350 2 2 Tu1 Te5 Cd 10 1 0
## 351 2 2 Tu1 Te5 water 1 47 10
## 352 2 2 Tu1 Te5 water 2 51 91
## 353 2 2 Tu1 Te5 water 3 0 0
## 354 2 2 Tu1 Te5 water 4 18 15
## 355 2 2 Tu1 Te5 water 5 0 0
## 356 2 2 Tu1 Te5 water 6 10 12
## 357 2 2 Tu1 Te5 water 7 1 18
## 358 2 2 Tu1 Te5 water 8 10 7
## 359 2 2 Tu1 Te5 water 9 26 31
## 360 2 2 Tu1 Te5 water 10 17 99
## 361 2 3 Tu1 Te4 Cd 1 4 0
## 362 2 3 Tu1 Te4 Cd 2 24 0
## 363 2 3 Tu1 Te4 Cd 3 22 1
## 364 2 3 Tu1 Te4 Cd 4 14 0
## 365 2 3 Tu1 Te4 Cd 5 4 0
## 366 2 3 Tu1 Te4 Cd 6 34 0
## 367 2 3 Tu1 Te4 Cd 7 0 0
## 368 2 3 Tu1 Te4 Cd 8 15 0
## 369 2 3 Tu1 Te4 Cd 9 1 0
## 370 2 3 Tu1 Te4 Cd 10 0 0
## 371 2 3 Tu1 Te4 water 1 0 0
## 372 2 3 Tu1 Te4 water 2 0 0
## 373 2 3 Tu1 Te4 water 3 0 13
## 374 2 3 Tu1 Te4 water 4 1 3
## 375 2 3 Tu1 Te4 water 5 0 0
## 376 2 3 Tu1 Te4 water 6 0 0
## 377 2 3 Tu1 Te4 water 7 0 0
## 378 2 3 Tu1 Te4 water 8 0 0
## 379 2 3 Tu1 Te4 water 9 6 8
## 380 2 3 Tu1 Te4 water 10 16 2
## 381 2 3 Tu1 Te5 Cd 1 59 8
## 382 2 3 Tu1 Te5 Cd 2 67 0
## 383 2 3 Tu1 Te5 Cd 3 0 0
## 384 2 3 Tu1 Te5 Cd 4 38 0
## 385 2 3 Tu1 Te5 Cd 5 10 25
## 386 2 3 Tu1 Te5 Cd 6 0 0
## 387 2 3 Tu1 Te5 Cd 7 0 2
## 388 2 3 Tu1 Te5 Cd 8 65 4
## 389 2 3 Tu1 Te5 Cd 9 0 0
## 390 2 3 Tu1 Te5 Cd 10 3 0
## 391 2 3 Tu1 Te5 water 1 1 2
## 392 2 3 Tu1 Te5 water 2 21 125
## 393 2 3 Tu1 Te5 water 3 1 0
## 394 2 3 Tu1 Te5 water 4 2 3
## 395 2 3 Tu1 Te5 water 5 0 0
## 396 2 3 Tu1 Te5 water 6 54 11
## 397 2 3 Tu1 Te5 water 7 0 4
## 398 2 3 Tu1 Te5 water 8 1 0
## 399 2 3 Tu1 Te5 water 9 47 23
## 400 2 3 Tu1 Te5 water 10 16 25
## 401 2 4 Tu1 Te4 Cd 1 17 0
## 402 2 4 Tu1 Te4 Cd 2 6 0
## 403 2 4 Tu1 Te4 Cd 3 4 0
## 404 2 4 Tu1 Te4 Cd 4 76 0
## 405 2 4 Tu1 Te4 Cd 5 13 0
## 406 2 4 Tu1 Te4 Cd 6 4 0
## 407 2 4 Tu1 Te4 Cd 7 44 0
## 408 2 4 Tu1 Te4 Cd 8 40 0
## 409 2 4 Tu1 Te4 Cd 9 0 0
## 410 2 4 Tu1 Te4 Cd 10 6 0
## 411 2 4 Tu1 Te4 water 1 0 0
## 412 2 4 Tu1 Te4 water 2 0 0
## 413 2 4 Tu1 Te4 water 3 1 22
## 414 2 4 Tu1 Te4 water 4 0 1
## 415 2 4 Tu1 Te4 water 5 0 0
## 416 2 4 Tu1 Te4 water 6 3 0
## 417 2 4 Tu1 Te4 water 7 2 1
## 418 2 4 Tu1 Te4 water 8 2 0
## 419 2 4 Tu1 Te4 water 9 42 68
## 420 2 4 Tu1 Te4 water 10 3 2
## 421 2 4 Tu1 Te5 Cd 1 54 17
## 422 2 4 Tu1 Te5 Cd 2 38 0
## 423 2 4 Tu1 Te5 Cd 3 77 2
## 424 2 4 Tu1 Te5 Cd 4 38 0
## 425 2 4 Tu1 Te5 Cd 5 2 0
## 426 2 4 Tu1 Te5 Cd 6 21 5
## 427 2 4 Tu1 Te5 Cd 7 12 0
## 428 2 4 Tu1 Te5 Cd 8 36 0
## 429 2 4 Tu1 Te5 Cd 9 0 0
## 430 2 4 Tu1 Te5 Cd 10 21 0
## 431 2 4 Tu1 Te5 water 1 0 0
## 432 2 4 Tu1 Te5 water 2 122 188
## 433 2 4 Tu1 Te5 water 3 0 0
## 434 2 4 Tu1 Te5 water 4 40 24
## 435 2 4 Tu1 Te5 water 5 7 3
## 436 2 4 Tu1 Te5 water 6 26 27
## 437 2 4 Tu1 Te5 water 7 5 18
## 438 2 4 Tu1 Te5 water 8 0 7
## 439 2 4 Tu1 Te5 water 9 45 70
## 440 2 4 Tu1 Te5 water 10 4 3
## 441 2 5 Tu1 Te4 Cd 1 11 0
## 442 2 5 Tu1 Te4 Cd 2 88 0
## 443 2 5 Tu1 Te4 Cd 3 87 0
## 444 2 5 Tu1 Te4 Cd 4 9 0
## 445 2 5 Tu1 Te4 Cd 5 29 0
## 446 2 5 Tu1 Te4 Cd 6 57 0
## 447 2 5 Tu1 Te4 Cd 7 180 0
## 448 2 5 Tu1 Te4 Cd 8 117 0
## 449 2 5 Tu1 Te4 Cd 9 8 0
## 450 2 5 Tu1 Te4 Cd 10 30 0
## 451 2 5 Tu1 Te4 water 1 0 0
## 452 2 5 Tu1 Te4 water 2 0 0
## 453 2 5 Tu1 Te4 water 3 49 66
## 454 2 5 Tu1 Te4 water 4 2 0
## 455 2 5 Tu1 Te4 water 5 0 0
## 456 2 5 Tu1 Te4 water 6 0 0
## 457 2 5 Tu1 Te4 water 7 7 3
## 458 2 5 Tu1 Te4 water 8 43 13
## 459 2 5 Tu1 Te4 water 9 40 26
## 460 2 5 Tu1 Te4 water 10 170 58
## 461 2 5 Tu1 Te5 Cd 1 23 2
## 462 2 5 Tu1 Te5 Cd 2 85 0
## 463 2 5 Tu1 Te5 Cd 3 10 0
## 464 2 5 Tu1 Te5 Cd 4 31 1
## 465 2 5 Tu1 Te5 Cd 5 81 27
## 466 2 5 Tu1 Te5 Cd 6 11 1
## 467 2 5 Tu1 Te5 Cd 7 26 2
## 468 2 5 Tu1 Te5 Cd 8 139 7
## 469 2 5 Tu1 Te5 Cd 9 40 0
## 470 2 5 Tu1 Te5 Cd 10 10 0
## 471 2 5 Tu1 Te5 water 1 44 4
## 472 2 5 Tu1 Te5 water 2 68 74
## 473 2 5 Tu1 Te5 water 3 6 0
## 474 2 5 Tu1 Te5 water 4 2 20
## 475 2 5 Tu1 Te5 water 5 0 0
## 476 2 5 Tu1 Te5 water 6 42 45
## 477 2 5 Tu1 Te5 water 7 54 100
## 478 2 5 Tu1 Te5 water 8 24 38
## 479 2 5 Tu1 Te5 water 9 43 70
## 480 2 5 Tu1 Te5 water 10 191 108
## 481 3 2 Tu1 Te4 Cd 1 31 2
## 482 3 2 Tu1 Te4 Cd 2 9 12
## 483 3 2 Tu1 Te4 Cd 3 75 0
## 484 3 2 Tu1 Te4 Cd 4 21 5
## 485 3 2 Tu1 Te4 Cd 5 56 0
## 486 3 2 Tu1 Te4 Cd 6 11 0
## 487 3 2 Tu1 Te4 Cd 7 1 0
## 488 3 2 Tu1 Te4 Cd 8 287 2
## 489 3 2 Tu1 Te4 Cd 9 12 0
## 490 3 2 Tu1 Te4 Cd 10 14 0
## 491 3 2 Tu1 Te4 water 1 3 0
## 492 3 2 Tu1 Te4 water 2 6 0
## 493 3 2 Tu1 Te4 water 3 0 0
## 494 3 2 Tu1 Te4 water 4 0 0
## 495 3 2 Tu1 Te4 water 5 120 14
## 496 3 2 Tu1 Te4 water 6 17 42
## 497 3 2 Tu1 Te4 water 7 202 20
## 498 3 2 Tu1 Te4 water 8 74 11
## 499 3 2 Tu1 Te4 water 9 76 64
## 500 3 2 Tu1 Te4 water 10 159 21
## 501 3 2 Tu1 Te5 Cd 1 29 0
## 502 3 2 Tu1 Te5 Cd 2 28 0
## 503 3 2 Tu1 Te5 Cd 3 50 0
## 504 3 2 Tu1 Te5 Cd 4 87 0
## 505 3 2 Tu1 Te5 Cd 5 52 0
## 506 3 2 Tu1 Te5 Cd 6 45 6
## 507 3 2 Tu1 Te5 Cd 7 116 0
## 508 3 2 Tu1 Te5 Cd 8 60 0
## 509 3 2 Tu1 Te5 Cd 9 65 3
## 510 3 2 Tu1 Te5 Cd 10 106 22
## 511 3 2 Tu1 Te5 water 1 0 0
## 512 3 2 Tu1 Te5 water 2 0 0
## 513 3 2 Tu1 Te5 water 3 0 0
## 514 3 2 Tu1 Te5 water 4 0 0
## 515 3 2 Tu1 Te5 water 5 0 0
## 516 3 2 Tu1 Te5 water 6 0 0
## 517 3 2 Tu1 Te5 water 7 0 0
## 518 3 2 Tu1 Te5 water 8 46 0
## 519 3 2 Tu1 Te5 water 9 0 0
## 520 3 2 Tu1 Te5 water 10 0 0
## 521 3 2 Tu2 Te4 Cd 1 129 34
## 522 3 2 Tu2 Te4 Cd 2 46 5
## 523 3 2 Tu2 Te4 Cd 3 36 4
## 524 3 2 Tu2 Te4 Cd 4 87 4
## 525 3 2 Tu2 Te4 Cd 5 0 0
## 526 3 2 Tu2 Te4 Cd 6 13 8
## 527 3 2 Tu2 Te4 Cd 7 11 7
## 528 3 2 Tu2 Te4 Cd 8 32 1
## 529 3 2 Tu2 Te4 Cd 9 8 4
## 530 3 2 Tu2 Te4 Cd 10 60 9
## 531 3 2 Tu2 Te4 water 1 84 23
## 532 3 2 Tu2 Te4 water 2 51 11
## 533 3 2 Tu2 Te4 water 3 1 0
## 534 3 2 Tu2 Te4 water 4 107 2
## 535 3 2 Tu2 Te4 water 5 112 4
## 536 3 2 Tu2 Te4 water 6 0 0
## 537 3 2 Tu2 Te4 water 7 51 18
## 538 3 2 Tu2 Te4 water 8 0 0
## 539 3 2 Tu2 Te4 water 9 0 0
## 540 3 2 Tu2 Te4 water 10 0 3
## 541 3 2 Tu2 Te5 Cd 1 95 0
## 542 3 2 Tu2 Te5 Cd 2 96 1
## 543 3 2 Tu2 Te5 Cd 3 113 17
## 544 3 2 Tu2 Te5 Cd 4 20 0
## 545 3 2 Tu2 Te5 Cd 5 69 19
## 546 3 2 Tu2 Te5 Cd 6 61 0
## 547 3 2 Tu2 Te5 Cd 7 202 15
## 548 3 2 Tu2 Te5 Cd 8 17 0
## 549 3 2 Tu2 Te5 Cd 9 24 0
## 550 3 2 Tu2 Te5 Cd 10 3 0
## 551 3 2 Tu2 Te5 water 1 1 0
## 552 3 2 Tu2 Te5 water 2 1 0
## 553 3 2 Tu2 Te5 water 3 0 0
## 554 3 2 Tu2 Te5 water 4 6 17
## 555 3 2 Tu2 Te5 water 5 0 0
## 556 3 2 Tu2 Te5 water 6 59 17
## 557 3 2 Tu2 Te5 water 7 210 14
## 558 3 2 Tu2 Te5 water 8 78 21
## 559 3 2 Tu2 Te5 water 9 48 2
## 560 3 2 Tu2 Te5 water 10 4 1
## 561 3 3 Tu1 Te4 Cd 1 100 1
## 562 3 3 Tu1 Te4 Cd 2 32 7
## 563 3 3 Tu1 Te4 Cd 3 47 4
## 564 3 3 Tu1 Te4 Cd 4 31 2
## 565 3 3 Tu1 Te4 Cd 5 31 0
## 566 3 3 Tu1 Te4 Cd 6 80 0
## 567 3 3 Tu1 Te4 Cd 7 0 0
## 568 3 3 Tu1 Te4 Cd 8 20 0
## 569 3 3 Tu1 Te4 Cd 9 4 0
## 570 3 3 Tu1 Te4 Cd 10 16 0
## 571 3 3 Tu1 Te4 water 1 3 0
## 572 3 3 Tu1 Te4 water 2 5 0
## 573 3 3 Tu1 Te4 water 3 0 0
## 574 3 3 Tu1 Te4 water 4 0 0
## 575 3 3 Tu1 Te4 water 5 5 0
## 576 3 3 Tu1 Te4 water 6 75 61
## 577 3 3 Tu1 Te4 water 7 84 41
## 578 3 3 Tu1 Te4 water 8 26 39
## 579 3 3 Tu1 Te4 water 9 88 52
## 580 3 3 Tu1 Te4 water 10 90 15
## 581 3 3 Tu1 Te5 Cd 1 10 0
## 582 3 3 Tu1 Te5 Cd 2 15 0
## 583 3 3 Tu1 Te5 Cd 3 34 0
## 584 3 3 Tu1 Te5 Cd 4 31 1
## 585 3 3 Tu1 Te5 Cd 5 57 0
## 586 3 3 Tu1 Te5 Cd 6 0 0
## 587 3 3 Tu1 Te5 Cd 7 0 0
## 588 3 3 Tu1 Te5 Cd 8 0 0
## 589 3 3 Tu1 Te5 Cd 9 37 2
## 590 3 3 Tu1 Te5 Cd 10 0 0
## 591 3 3 Tu1 Te5 water 1 0 0
## 592 3 3 Tu1 Te5 water 2 0 0
## 593 3 3 Tu1 Te5 water 3 0 0
## 594 3 3 Tu1 Te5 water 4 0 0
## 595 3 3 Tu1 Te5 water 5 0 0
## 596 3 3 Tu1 Te5 water 6 0 0
## 597 3 3 Tu1 Te5 water 7 0 0
## 598 3 3 Tu1 Te5 water 8 0 0
## 599 3 3 Tu1 Te5 water 9 0 0
## 600 3 3 Tu1 Te5 water 10 0 0
## 601 3 3 Tu2 Te4 Cd 1 2 0
## 602 3 3 Tu2 Te4 Cd 2 0 0
## 603 3 3 Tu2 Te4 Cd 3 19 6
## 604 3 3 Tu2 Te4 Cd 4 21 1
## 605 3 3 Tu2 Te4 Cd 5 0 0
## 606 3 3 Tu2 Te4 Cd 6 1 1
## 607 3 3 Tu2 Te4 Cd 7 16 1
## 608 3 3 Tu2 Te4 Cd 8 31 0
## 609 3 3 Tu2 Te4 Cd 9 0 0
## 610 3 3 Tu2 Te4 Cd 10 61 1
## 611 3 3 Tu2 Te4 water 1 1 3
## 612 3 3 Tu2 Te4 water 2 62 13
## 613 3 3 Tu2 Te4 water 3 72 71
## 614 3 3 Tu2 Te4 water 4 211 12
## 615 3 3 Tu2 Te4 water 5 15 10
## 616 3 3 Tu2 Te4 water 6 0 0
## 617 3 3 Tu2 Te4 water 7 0 0
## 618 3 3 Tu2 Te4 water 8 0 0
## 619 3 3 Tu2 Te4 water 9 0 0
## 620 3 3 Tu2 Te4 water 10 0 0
## 621 3 3 Tu2 Te5 Cd 1 32 3
## 622 3 3 Tu2 Te5 Cd 2 0 0
## 623 3 3 Tu2 Te5 Cd 3 111 2
## 624 3 3 Tu2 Te5 Cd 4 12 0
## 625 3 3 Tu2 Te5 Cd 5 2 0
## 626 3 3 Tu2 Te5 Cd 6 1 0
## 627 3 3 Tu2 Te5 Cd 7 1 1
## 628 3 3 Tu2 Te5 Cd 8 0 0
## 629 3 3 Tu2 Te5 Cd 9 0 0
## 630 3 3 Tu2 Te5 Cd 10 3 0
## 631 3 3 Tu2 Te5 water 1 0 0
## 632 3 3 Tu2 Te5 water 2 0 0
## 633 3 3 Tu2 Te5 water 3 0 0
## 634 3 3 Tu2 Te5 water 4 2 0
## 635 3 3 Tu2 Te5 water 5 0 0
## 636 3 3 Tu2 Te5 water 6 0 0
## 637 3 3 Tu2 Te5 water 7 0 0
## 638 3 3 Tu2 Te5 water 8 0 0
## 639 3 3 Tu2 Te5 water 9 25 34
## 640 3 3 Tu2 Te5 water 10 8 38
## 641 3 4 Tu1 Te4 Cd 1 6 1
## 642 3 4 Tu1 Te4 Cd 2 22 20
## 643 3 4 Tu1 Te4 Cd 3 109 2
## 644 3 4 Tu1 Te4 Cd 4 86 4
## 645 3 4 Tu1 Te4 Cd 5 68 1
## 646 3 4 Tu1 Te4 Cd 6 229 0
## 647 3 4 Tu1 Te4 Cd 7 5 0
## 648 3 4 Tu1 Te4 Cd 8 89 0
## 649 3 4 Tu1 Te4 Cd 9 1 0
## 650 3 4 Tu1 Te4 Cd 10 0 0
## 651 3 4 Tu1 Te4 water 1 1 1
## 652 3 4 Tu1 Te4 water 2 0 0
## 653 3 4 Tu1 Te4 water 3 0 0
## 654 3 4 Tu1 Te4 water 4 0 0
## 655 3 4 Tu1 Te4 water 5 39 11
## 656 3 4 Tu1 Te4 water 6 112 37
## 657 3 4 Tu1 Te4 water 7 0 0
## 658 3 4 Tu1 Te4 water 8 58 12
## 659 3 4 Tu1 Te4 water 9 0 0
## 660 3 4 Tu1 Te4 water 10 0 0
## 661 3 4 Tu1 Te5 Cd 1 106 0
## 662 3 4 Tu1 Te5 Cd 2 55 0
## 663 3 4 Tu1 Te5 Cd 3 39 0
## 664 3 4 Tu1 Te5 Cd 4 81 0
## 665 3 4 Tu1 Te5 Cd 5 125 0
## 666 3 4 Tu1 Te5 Cd 6 3 0
## 667 3 4 Tu1 Te5 Cd 7 46 0
## 668 3 4 Tu1 Te5 Cd 8 152 0
## 669 3 4 Tu1 Te5 Cd 9 124 2
## 670 3 4 Tu1 Te5 Cd 10 0 0
## 671 3 4 Tu1 Te5 water 1 0 0
## 672 3 4 Tu1 Te5 water 2 0 0
## 673 3 4 Tu1 Te5 water 3 0 0
## 674 3 4 Tu1 Te5 water 4 0 0
## 675 3 4 Tu1 Te5 water 5 0 0
## 676 3 4 Tu1 Te5 water 6 0 0
## 677 3 4 Tu1 Te5 water 7 0 0
## 678 3 4 Tu1 Te5 water 8 6 0
## 679 3 4 Tu1 Te5 water 9 0 0
## 680 3 4 Tu1 Te5 water 10 0 0
## 681 3 4 Tu2 Te4 Cd 1 0 0
## 682 3 4 Tu2 Te4 Cd 2 1 0
## 683 3 4 Tu2 Te4 Cd 3 22 6
## 684 3 4 Tu2 Te4 Cd 4 0 0
## 685 3 4 Tu2 Te4 Cd 5 0 0
## 686 3 4 Tu2 Te4 Cd 6 0 0
## 687 3 4 Tu2 Te4 Cd 7 31 29
## 688 3 4 Tu2 Te4 Cd 8 42 4
## 689 3 4 Tu2 Te4 Cd 9 9 3
## 690 3 4 Tu2 Te4 Cd 10 0 0
## 691 3 4 Tu2 Te4 water 1 0 0
## 692 3 4 Tu2 Te4 water 2 14 3
## 693 3 4 Tu2 Te4 water 3 0 0
## 694 3 4 Tu2 Te4 water 4 150 8
## 695 3 4 Tu2 Te4 water 5 96 65
## 696 3 4 Tu2 Te4 water 6 0 0
## 697 3 4 Tu2 Te4 water 7 1 0
## 698 3 4 Tu2 Te4 water 8 0 0
## 699 3 4 Tu2 Te4 water 9 0 0
## 700 3 4 Tu2 Te4 water 10 2 10
## 701 3 4 Tu2 Te5 Cd 1 143 20
## 702 3 4 Tu2 Te5 Cd 2 226 2
## 703 3 4 Tu2 Te5 Cd 3 0 0
## 704 3 4 Tu2 Te5 Cd 4 121 7
## 705 3 4 Tu2 Te5 Cd 5 42 16
## 706 3 4 Tu2 Te5 Cd 6 223 3
## 707 3 4 Tu2 Te5 Cd 7 2 1
## 708 3 4 Tu2 Te5 Cd 8 60 1
## 709 3 4 Tu2 Te5 Cd 9 31 0
## 710 3 4 Tu2 Te5 Cd 10 5 0
## 711 3 4 Tu2 Te5 water 1 0 0
## 712 3 4 Tu2 Te5 water 2 3 0
## 713 3 4 Tu2 Te5 water 3 0 0
## 714 3 4 Tu2 Te5 water 4 0 0
## 715 3 4 Tu2 Te5 water 5 0 0
## 716 3 4 Tu2 Te5 water 6 36 31
## 717 3 4 Tu2 Te5 water 7 0 0
## 718 3 4 Tu2 Te5 water 8 52 62
## 719 3 4 Tu2 Te5 water 9 0 0
## 720 3 4 Tu2 Te5 water 10 92 26
## 721 3 5 Tu1 Te4 Cd 1 199 8
## 722 3 5 Tu1 Te4 Cd 2 97 47
## 723 3 5 Tu1 Te4 Cd 3 210 4
## 724 3 5 Tu1 Te4 Cd 4 115 6
## 725 3 5 Tu1 Te4 Cd 5 308 0
## 726 3 5 Tu1 Te4 Cd 6 237 0
## 727 3 5 Tu1 Te4 Cd 7 0 1
## 728 3 5 Tu1 Te4 Cd 8 2 0
## 729 3 5 Tu1 Te4 Cd 9 12 0
## 730 3 5 Tu1 Te4 Cd 10 189 0
## 731 3 5 Tu1 Te4 water 1 60 0
## 732 3 5 Tu1 Te4 water 2 8 0
## 733 3 5 Tu1 Te4 water 3 0 0
## 734 3 5 Tu1 Te4 water 4 0 0
## 735 3 5 Tu1 Te4 water 5 266 38
## 736 3 5 Tu1 Te4 water 6 100 77
## 737 3 5 Tu1 Te4 water 7 289 73
## 738 3 5 Tu1 Te4 water 8 55 92
## 739 3 5 Tu1 Te4 water 9 251 214
## 740 3 5 Tu1 Te4 water 10 215 103
## 741 3 5 Tu1 Te5 Cd 1 26 0
## 742 3 5 Tu1 Te5 Cd 2 74 0
## 743 3 5 Tu1 Te5 Cd 3 69 0
## 744 3 5 Tu1 Te5 Cd 4 98 1
## 745 3 5 Tu1 Te5 Cd 5 213 0
## 746 3 5 Tu1 Te5 Cd 6 21 1
## 747 3 5 Tu1 Te5 Cd 7 301 3
## 748 3 5 Tu1 Te5 Cd 8 207 0
## 749 3 5 Tu1 Te5 Cd 9 210 9
## 750 3 5 Tu1 Te5 Cd 10 312 7
## 751 3 5 Tu1 Te5 water 1 1 0
## 752 3 5 Tu1 Te5 water 2 0 0
## 753 3 5 Tu1 Te5 water 3 3 1
## 754 3 5 Tu1 Te5 water 4 0 0
## 755 3 5 Tu1 Te5 water 5 0 0
## 756 3 5 Tu1 Te5 water 6 0 0
## 757 3 5 Tu1 Te5 water 7 0 0
## 758 3 5 Tu1 Te5 water 8 13 0
## 759 3 5 Tu1 Te5 water 9 0 0
## 760 3 5 Tu1 Te5 water 10 0 0
## 761 3 5 Tu2 Te4 Cd 1 4 9
## 762 3 5 Tu2 Te4 Cd 2 127 65
## 763 3 5 Tu2 Te4 Cd 3 181 10
## 764 3 5 Tu2 Te4 Cd 4 296 21
## 765 3 5 Tu2 Te4 Cd 5 2 0
## 766 3 5 Tu2 Te4 Cd 6 26 56
## 767 3 5 Tu2 Te4 Cd 7 134 40
## 768 3 5 Tu2 Te4 Cd 8 231 10
## 769 3 5 Tu2 Te4 Cd 9 168 11
## 770 3 5 Tu2 Te4 Cd 10 170 18
## 771 3 5 Tu2 Te4 water 1 180 211
## 772 3 5 Tu2 Te4 water 2 121 107
## 773 3 5 Tu2 Te4 water 3 302 220
## 774 3 5 Tu2 Te4 water 4 340 87
## 775 3 5 Tu2 Te4 water 5 170 78
## 776 3 5 Tu2 Te4 water 6 0 0
## 777 3 5 Tu2 Te4 water 7 262 99
## 778 3 5 Tu2 Te4 water 8 0 0
## 779 3 5 Tu2 Te4 water 9 0 0
## 780 3 5 Tu2 Te4 water 10 112 31
## 781 3 5 Tu2 Te5 Cd 1 223 6
## 782 3 5 Tu2 Te5 Cd 2 214 0
## 783 3 5 Tu2 Te5 Cd 3 204 10
## 784 3 5 Tu2 Te5 Cd 4 6 2
## 785 3 5 Tu2 Te5 Cd 5 161 35
## 786 3 5 Tu2 Te5 Cd 6 564 17
## 787 3 5 Tu2 Te5 Cd 7 287 17
## 788 3 5 Tu2 Te5 Cd 8 349 3
## 789 3 5 Tu2 Te5 Cd 9 210 1
## 790 3 5 Tu2 Te5 Cd 10 6 0
## 791 3 5 Tu2 Te5 water 1 0 0
## 792 3 5 Tu2 Te5 water 2 0 0
## 793 3 5 Tu2 Te5 water 3 49 12
## 794 3 5 Tu2 Te5 water 4 7 4
## 795 3 5 Tu2 Te5 water 5 0 0
## 796 3 5 Tu2 Te5 water 6 87 93
## 797 3 5 Tu2 Te5 water 7 298 56
## 798 3 5 Tu2 Te5 water 8 65 49
## 799 3 5 Tu2 Te5 water 9 121 59
## 800 3 5 Tu2 Te5 water 10 64 59
## 801 4 2 Tu1 Te4 Cd 1 16 2
## 802 4 2 Tu1 Te4 Cd 2 48 0
## 803 4 2 Tu1 Te4 Cd 3 119 1
## 804 4 2 Tu1 Te4 Cd 4 8 2
## 805 4 2 Tu1 Te4 Cd 5 19 0
## 806 4 2 Tu1 Te4 Cd 6 0 0
## 807 4 2 Tu1 Te4 Cd 7 87 0
## 808 4 2 Tu1 Te4 Cd 8 62 1
## 809 4 2 Tu1 Te4 Cd 9 23 2
## 810 4 2 Tu1 Te4 Cd 10 5 0
## 811 4 2 Tu1 Te4 water 1 157 64
## 812 4 2 Tu1 Te4 water 2 54 34
## 813 4 2 Tu1 Te4 water 3 65 7
## 814 4 2 Tu1 Te4 water 4 14 0
## 815 4 2 Tu1 Te4 water 5 35 22
## 816 4 2 Tu1 Te4 water 6 271 37
## 817 4 2 Tu1 Te4 water 7 336 40
## 818 4 2 Tu1 Te4 water 8 358 42
## 819 4 2 Tu1 Te4 water 9 224 11
## 820 4 2 Tu1 Te4 water 10 6 0
## 821 4 2 Tu1 Te5 Cd 1 241 5
## 822 4 2 Tu1 Te5 Cd 2 106 9
## 823 4 2 Tu1 Te5 Cd 3 1 0
## 824 4 2 Tu1 Te5 Cd 4 72 0
## 825 4 2 Tu1 Te5 Cd 5 123 0
## 826 4 2 Tu1 Te5 Cd 6 109 1
## 827 4 2 Tu1 Te5 Cd 7 102 1
## 828 4 2 Tu1 Te5 Cd 8 21 15
## 829 4 2 Tu1 Te5 Cd 9 78 4
## 830 4 2 Tu1 Te5 Cd 10 20 1
## 831 4 2 Tu1 Te5 water 1 0 0
## 832 4 2 Tu1 Te5 water 2 2 0
## 833 4 2 Tu1 Te5 water 3 0 2
## 834 4 2 Tu1 Te5 water 4 0 2
## 835 4 2 Tu1 Te5 water 5 1 0
## 836 4 2 Tu1 Te5 water 6 0 0
## 837 4 2 Tu1 Te5 water 7 0 0
## 838 4 2 Tu1 Te5 water 8 0 0
## 839 4 2 Tu1 Te5 water 9 0 0
## 840 4 2 Tu1 Te5 water 10 0 0
## 841 4 2 Tu2 Te4 Cd 1 26 2
## 842 4 2 Tu2 Te4 Cd 2 27 0
## 843 4 2 Tu2 Te4 Cd 3 6 1
## 844 4 2 Tu2 Te4 Cd 4 54 3
## 845 4 2 Tu2 Te4 Cd 5 16 3
## 846 4 2 Tu2 Te4 Cd 6 61 4
## 847 4 2 Tu2 Te4 Cd 7 36 1
## 848 4 2 Tu2 Te4 Cd 8 21 1
## 849 4 2 Tu2 Te4 Cd 9 87 9
## 850 4 2 Tu2 Te4 Cd 10 10 1
## 851 4 2 Tu2 Te4 water 1 0 0
## 852 4 2 Tu2 Te4 water 2 0 0
## 853 4 2 Tu2 Te4 water 3 10 0
## 854 4 2 Tu2 Te4 water 4 3 0
## 855 4 2 Tu2 Te4 water 5 8 0
## 856 4 2 Tu2 Te4 water 6 175 78
## 857 4 2 Tu2 Te4 water 7 9 2
## 858 4 2 Tu2 Te4 water 8 138 25
## 859 4 2 Tu2 Te4 water 9 108 83
## 860 4 2 Tu2 Te4 water 10 1 0
## 861 4 2 Tu2 Te5 Cd 1 19 0
## 862 4 2 Tu2 Te5 Cd 2 122 0
## 863 4 2 Tu2 Te5 Cd 3 113 14
## 864 4 2 Tu2 Te5 Cd 4 22 2
## 865 4 2 Tu2 Te5 Cd 5 28 12
## 866 4 2 Tu2 Te5 Cd 6 1 8
## 867 4 2 Tu2 Te5 Cd 7 54 3
## 868 4 2 Tu2 Te5 Cd 8 1 0
## 869 4 2 Tu2 Te5 Cd 9 52 1
## 870 4 2 Tu2 Te5 Cd 10 55 0
## 871 4 2 Tu2 Te5 water 1 0 0
## 872 4 2 Tu2 Te5 water 2 0 0
## 873 4 2 Tu2 Te5 water 3 0 0
## 874 4 2 Tu2 Te5 water 4 0 0
## 875 4 2 Tu2 Te5 water 5 0 0
## 876 4 2 Tu2 Te5 water 6 1 0
## 877 4 2 Tu2 Te5 water 7 0 0
## 878 4 2 Tu2 Te5 water 8 1 1
## 879 4 2 Tu2 Te5 water 9 4 0
## 880 4 2 Tu2 Te5 water 10 0 0
## 881 4 3 Tu1 Te4 Cd 1 0 0
## 882 4 3 Tu1 Te4 Cd 2 0 0
## 883 4 3 Tu1 Te4 Cd 3 5 0
## 884 4 3 Tu1 Te4 Cd 4 62 6
## 885 4 3 Tu1 Te4 Cd 5 18 0
## 886 4 3 Tu1 Te4 Cd 6 23 0
## 887 4 3 Tu1 Te4 Cd 7 39 1
## 888 4 3 Tu1 Te4 Cd 8 131 1
## 889 4 3 Tu1 Te4 Cd 9 25 0
## 890 4 3 Tu1 Te4 Cd 10 8 0
## 891 4 3 Tu1 Te4 water 1 0 0
## 892 4 3 Tu1 Te4 water 2 0 0
## 893 4 3 Tu1 Te4 water 3 1 0
## 894 4 3 Tu1 Te4 water 4 6 1
## 895 4 3 Tu1 Te4 water 5 0 0
## 896 4 3 Tu1 Te4 water 6 10 4
## 897 4 3 Tu1 Te4 water 7 157 146
## 898 4 3 Tu1 Te4 water 8 2 1
## 899 4 3 Tu1 Te4 water 9 3 1
## 900 4 3 Tu1 Te4 water 10 74 0
## 901 4 3 Tu1 Te5 Cd 1 0 0
## 902 4 3 Tu1 Te5 Cd 2 133 7
## 903 4 3 Tu1 Te5 Cd 3 0 0
## 904 4 3 Tu1 Te5 Cd 4 71 3
## 905 4 3 Tu1 Te5 Cd 5 29 0
## 906 4 3 Tu1 Te5 Cd 6 3 0
## 907 4 3 Tu1 Te5 Cd 7 33 0
## 908 4 3 Tu1 Te5 Cd 8 120 13
## 909 4 3 Tu1 Te5 Cd 9 104 5
## 910 4 3 Tu1 Te5 Cd 10 72 1
## 911 4 3 Tu1 Te5 water 1 3 10
## 912 4 3 Tu1 Te5 water 2 0 17
## 913 4 3 Tu1 Te5 water 3 1 6
## 914 4 3 Tu1 Te5 water 4 4 15
## 915 4 3 Tu1 Te5 water 5 1 1
## 916 4 3 Tu1 Te5 water 6 0 0
## 917 4 3 Tu1 Te5 water 7 0 0
## 918 4 3 Tu1 Te5 water 8 0 0
## 919 4 3 Tu1 Te5 water 9 0 0
## 920 4 3 Tu1 Te5 water 10 0 0
## 921 4 3 Tu2 Te4 Cd 1 4 0
## 922 4 3 Tu2 Te4 Cd 2 47 8
## 923 4 3 Tu2 Te4 Cd 3 30 0
## 924 4 3 Tu2 Te4 Cd 4 11 9
## 925 4 3 Tu2 Te4 Cd 5 24 0
## 926 4 3 Tu2 Te4 Cd 6 0 0
## 927 4 3 Tu2 Te4 Cd 7 25 2
## 928 4 3 Tu2 Te4 Cd 8 20 3
## 929 4 3 Tu2 Te4 Cd 9 20 6
## 930 4 3 Tu2 Te4 Cd 10 8 2
## 931 4 3 Tu2 Te4 water 1 0 0
## 932 4 3 Tu2 Te4 water 2 3 0
## 933 4 3 Tu2 Te4 water 3 26 77
## 934 4 3 Tu2 Te4 water 4 3 0
## 935 4 3 Tu2 Te4 water 5 0 0
## 936 4 3 Tu2 Te4 water 6 89 13
## 937 4 3 Tu2 Te4 water 7 0 0
## 938 4 3 Tu2 Te4 water 8 8 0
## 939 4 3 Tu2 Te4 water 9 57 9
## 940 4 3 Tu2 Te4 water 10 20 9
## 941 4 3 Tu2 Te5 Cd 1 16 0
## 942 4 3 Tu2 Te5 Cd 2 14 0
## 943 4 3 Tu2 Te5 Cd 3 62 6
## 944 4 3 Tu2 Te5 Cd 4 48 3
## 945 4 3 Tu2 Te5 Cd 5 99 15
## 946 4 3 Tu2 Te5 Cd 6 37 2
## 947 4 3 Tu2 Te5 Cd 7 5 0
## 948 4 3 Tu2 Te5 Cd 8 8 0
## 949 4 3 Tu2 Te5 Cd 9 128 3
## 950 4 3 Tu2 Te5 Cd 10 15 0
## 951 4 3 Tu2 Te5 water 1 0 0
## 952 4 3 Tu2 Te5 water 2 0 0
## 953 4 3 Tu2 Te5 water 3 0 0
## 954 4 3 Tu2 Te5 water 4 0 0
## 955 4 3 Tu2 Te5 water 5 0 0
## 956 4 3 Tu2 Te5 water 6 4 1
## 957 4 3 Tu2 Te5 water 7 0 0
## 958 4 3 Tu2 Te5 water 8 0 0
## 959 4 3 Tu2 Te5 water 9 69 6
## 960 4 3 Tu2 Te5 water 10 1 0
## 961 4 4 Tu1 Te4 Cd 1 310 7
## 962 4 4 Tu1 Te4 Cd 2 65 4
## 963 4 4 Tu1 Te4 Cd 3 272 24
## 964 4 4 Tu1 Te4 Cd 4 15 0
## 965 4 4 Tu1 Te4 Cd 5 165 0
## 966 4 4 Tu1 Te4 Cd 6 24 2
## 967 4 4 Tu1 Te4 Cd 7 98 0
## 968 4 4 Tu1 Te4 Cd 8 18 0
## 969 4 4 Tu1 Te4 Cd 9 55 0
## 970 4 4 Tu1 Te4 Cd 10 35 2
## 971 4 4 Tu1 Te4 water 1 10 1
## 972 4 4 Tu1 Te4 water 2 228 167
## 973 4 4 Tu1 Te4 water 3 0 0
## 974 4 4 Tu1 Te4 water 4 2 10
## 975 4 4 Tu1 Te4 water 5 2 3
## 976 4 4 Tu1 Te4 water 6 4 3
## 977 4 4 Tu1 Te4 water 7 102 19
## 978 4 4 Tu1 Te4 water 8 48 25
## 979 4 4 Tu1 Te4 water 9 0 0
## 980 4 4 Tu1 Te4 water 10 14 2
## 981 4 4 Tu1 Te5 Cd 1 0 0
## 982 4 4 Tu1 Te5 Cd 2 44 1
## 983 4 4 Tu1 Te5 Cd 3 198 10
## 984 4 4 Tu1 Te5 Cd 4 12 2
## 985 4 4 Tu1 Te5 Cd 5 24 0
## 986 4 4 Tu1 Te5 Cd 6 14 0
## 987 4 4 Tu1 Te5 Cd 7 50 1
## 988 4 4 Tu1 Te5 Cd 8 0 0
## 989 4 4 Tu1 Te5 Cd 9 123 7
## 990 4 4 Tu1 Te5 Cd 10 51 7
## 991 4 4 Tu1 Te5 water 1 0 0
## 992 4 4 Tu1 Te5 water 2 0 0
## 993 4 4 Tu1 Te5 water 3 1 2
## 994 4 4 Tu1 Te5 water 4 2 20
## 995 4 4 Tu1 Te5 water 5 2 6
## 996 4 4 Tu1 Te5 water 6 0 0
## 997 4 4 Tu1 Te5 water 7 0 0
## 998 4 4 Tu1 Te5 water 8 0 0
## 999 4 4 Tu1 Te5 water 9 0 0
## 1000 4 4 Tu1 Te5 water 10 0 0
## 1001 4 4 Tu2 Te4 Cd 1 33 1
## 1002 4 4 Tu2 Te4 Cd 2 28 4
## 1003 4 4 Tu2 Te4 Cd 3 0 0
## 1004 4 4 Tu2 Te4 Cd 4 75 15
## 1005 4 4 Tu2 Te4 Cd 5 20 1
## 1006 4 4 Tu2 Te4 Cd 6 97 26
## 1007 4 4 Tu2 Te4 Cd 7 23 8
## 1008 4 4 Tu2 Te4 Cd 8 6 6
## 1009 4 4 Tu2 Te4 Cd 9 75 1
## 1010 4 4 Tu2 Te4 Cd 10 129 9
## 1011 4 4 Tu2 Te4 water 1 0 0
## 1012 4 4 Tu2 Te4 water 2 0 0
## 1013 4 4 Tu2 Te4 water 3 0 0
## 1014 4 4 Tu2 Te4 water 4 0 0
## 1015 4 4 Tu2 Te4 water 5 0 0
## 1016 4 4 Tu2 Te4 water 6 284 48
## 1017 4 4 Tu2 Te4 water 7 6 1
## 1018 4 4 Tu2 Te4 water 8 235 9
## 1019 4 4 Tu2 Te4 water 9 8 2
## 1020 4 4 Tu2 Te4 water 10 73 12
## 1021 4 4 Tu2 Te5 Cd 1 19 0
## 1022 4 4 Tu2 Te5 Cd 2 124 3
## 1023 4 4 Tu2 Te5 Cd 3 9 0
## 1024 4 4 Tu2 Te5 Cd 4 55 1
## 1025 4 4 Tu2 Te5 Cd 5 141 22
## 1026 4 4 Tu2 Te5 Cd 6 9 22
## 1027 4 4 Tu2 Te5 Cd 7 24 0
## 1028 4 4 Tu2 Te5 Cd 8 14 0
## 1029 4 4 Tu2 Te5 Cd 9 30 6
## 1030 4 4 Tu2 Te5 Cd 10 108 3
## 1031 4 4 Tu2 Te5 water 1 0 0
## 1032 4 4 Tu2 Te5 water 2 0 0
## 1033 4 4 Tu2 Te5 water 3 0 0
## 1034 4 4 Tu2 Te5 water 4 0 0
## 1035 4 4 Tu2 Te5 water 5 0 0
## 1036 4 4 Tu2 Te5 water 6 1 0
## 1037 4 4 Tu2 Te5 water 7 0 0
## 1038 4 4 Tu2 Te5 water 8 0 0
## 1039 4 4 Tu2 Te5 water 9 14 1
## 1040 4 4 Tu2 Te5 water 10 0 0
## 1041 4 5 Tu1 Te4 Cd 1 2 10
## 1042 4 5 Tu1 Te4 Cd 2 81 11
## 1043 4 5 Tu1 Te4 Cd 3 12 0
## 1044 4 5 Tu1 Te4 Cd 4 49 1
## 1045 4 5 Tu1 Te4 Cd 5 84 0
## 1046 4 5 Tu1 Te4 Cd 6 67 5
## 1047 4 5 Tu1 Te4 Cd 7 135 0
## 1048 4 5 Tu1 Te4 Cd 8 428 7
## 1049 4 5 Tu1 Te4 Cd 9 112 1
## 1050 4 5 Tu1 Te4 Cd 10 20 1
## 1051 4 5 Tu1 Te4 water 1 210 15
## 1052 4 5 Tu1 Te4 water 2 151 18
## 1053 4 5 Tu1 Te4 water 3 17 1
## 1054 4 5 Tu1 Te4 water 4 205 27
## 1055 4 5 Tu1 Te4 water 5 132 66
## 1056 4 5 Tu1 Te4 water 6 206 506
## 1057 4 5 Tu1 Te4 water 7 416 89
## 1058 4 5 Tu1 Te4 water 8 561 414
## 1059 4 5 Tu1 Te4 water 9 0 0
## 1060 4 5 Tu1 Te4 water 10 248 12
## 1061 4 5 Tu1 Te5 Cd 1 189 2
## 1062 4 5 Tu1 Te5 Cd 2 124 17
## 1063 4 5 Tu1 Te5 Cd 3 331 6
## 1064 4 5 Tu1 Te5 Cd 4 259 1
## 1065 4 5 Tu1 Te5 Cd 5 490 3
## 1066 4 5 Tu1 Te5 Cd 6 428 12
## 1067 4 5 Tu1 Te5 Cd 7 136 0
## 1068 4 5 Tu1 Te5 Cd 8 69 24
## 1069 4 5 Tu1 Te5 Cd 9 322 7
## 1070 4 5 Tu1 Te5 Cd 10 325 5
## 1071 4 5 Tu1 Te5 water 1 32 118
## 1072 4 5 Tu1 Te5 water 2 56 67
## 1073 4 5 Tu1 Te5 water 3 37 30
## 1074 4 5 Tu1 Te5 water 4 222 84
## 1075 4 5 Tu1 Te5 water 5 1 6
## 1076 4 5 Tu1 Te5 water 6 0 0
## 1077 4 5 Tu1 Te5 water 7 0 0
## 1078 4 5 Tu1 Te5 water 8 0 0
## 1079 4 5 Tu1 Te5 water 9 0 0
## 1080 4 5 Tu1 Te5 water 10 336 3
## 1081 4 5 Tu2 Te4 Cd 1 102 0
## 1082 4 5 Tu2 Te4 Cd 2 172 6
## 1083 4 5 Tu2 Te4 Cd 3 22 1
## 1084 4 5 Tu2 Te4 Cd 4 21 32
## 1085 4 5 Tu2 Te4 Cd 5 163 4
## 1086 4 5 Tu2 Te4 Cd 6 328 40
## 1087 4 5 Tu2 Te4 Cd 7 78 12
## 1088 4 5 Tu2 Te4 Cd 8 219 12
## 1089 4 5 Tu2 Te4 Cd 9 484 15
## 1090 4 5 Tu2 Te4 Cd 10 232 23
## 1091 4 5 Tu2 Te4 water 1 318 64
## 1092 4 5 Tu2 Te4 water 2 298 8
## 1093 4 5 Tu2 Te4 water 3 98 270
## 1094 4 5 Tu2 Te4 water 4 4 0
## 1095 4 5 Tu2 Te4 water 5 17 0
## 1096 4 5 Tu2 Te4 water 6 217 16
## 1097 4 5 Tu2 Te4 water 7 4 13
## 1098 4 5 Tu2 Te4 water 8 198 31
## 1099 4 5 Tu2 Te4 water 9 483 16
## 1100 4 5 Tu2 Te4 water 10 166 107
## 1101 4 5 Tu2 Te5 Cd 1 115 7
## 1102 4 5 Tu2 Te5 Cd 2 220 7
## 1103 4 5 Tu2 Te5 Cd 3 123 19
## 1104 4 5 Tu2 Te5 Cd 4 163 29
## 1105 4 5 Tu2 Te5 Cd 5 157 28
## 1106 4 5 Tu2 Te5 Cd 6 39 6
## 1107 4 5 Tu2 Te5 Cd 7 37 8
## 1108 4 5 Tu2 Te5 Cd 8 11 0
## 1109 4 5 Tu2 Te5 Cd 9 48 16
## 1110 4 5 Tu2 Te5 Cd 10 117 1
## 1111 4 5 Tu2 Te5 water 1 0 0
## 1112 4 5 Tu2 Te5 water 2 0 0
## 1113 4 5 Tu2 Te5 water 3 0 0
## 1114 4 5 Tu2 Te5 water 4 0 0
## 1115 4 5 Tu2 Te5 water 5 0 0
## 1116 4 5 Tu2 Te5 water 6 11 1
## 1117 4 5 Tu2 Te5 water 7 1 0
## 1118 4 5 Tu2 Te5 water 8 0 0
## 1119 4 5 Tu2 Te5 water 9 383 12
## 1120 4 5 Tu2 Te5 water 10 147 0
## 1121 5 2 Tu1 Te4 Cd 1 8 0
## 1122 5 2 Tu1 Te4 Cd 2 5 0
## 1123 5 2 Tu1 Te4 Cd 3 27 0
## 1124 5 2 Tu1 Te4 Cd 4 17 0
## 1125 5 2 Tu1 Te4 Cd 5 1 0
## 1126 5 2 Tu1 Te4 Cd 6 82 20
## 1127 5 2 Tu1 Te4 Cd 7 10 1
## 1128 5 2 Tu1 Te4 Cd 8 9 0
## 1129 5 2 Tu1 Te4 Cd 9 55 0
## 1130 5 2 Tu1 Te4 Cd 10 77 21
## 1131 5 2 Tu1 Te4 water 1 57 11
## 1132 5 2 Tu1 Te4 water 2 21 22
## 1133 5 2 Tu1 Te4 water 3 30 75
## 1134 5 2 Tu1 Te4 water 4 95 44
## 1135 5 2 Tu1 Te4 water 5 74 18
## 1136 5 2 Tu1 Te4 water 6 0 0
## 1137 5 2 Tu1 Te4 water 7 34 35
## 1138 5 2 Tu1 Te4 water 8 147 82
## 1139 5 2 Tu1 Te4 water 9 121 29
## 1140 5 2 Tu1 Te4 water 10 206 28
## 1141 5 2 Tu1 Te5 Cd 1 3 0
## 1142 5 2 Tu1 Te5 Cd 2 141 9
## 1143 5 2 Tu1 Te5 Cd 3 40 0
## 1144 5 2 Tu1 Te5 Cd 4 55 0
## 1145 5 2 Tu1 Te5 Cd 5 125 3
## 1146 5 2 Tu1 Te5 Cd 6 28 0
## 1147 5 2 Tu1 Te5 Cd 7 32 0
## 1148 5 2 Tu1 Te5 Cd 8 6 0
## 1149 5 2 Tu1 Te5 Cd 9 20 0
## 1150 5 2 Tu1 Te5 Cd 10 62 0
## 1151 5 2 Tu1 Te5 water 1 59 47
## 1152 5 2 Tu1 Te5 water 2 94 10
## 1153 5 2 Tu1 Te5 water 3 58 26
## 1154 5 2 Tu1 Te5 water 4 39 0
## 1155 5 2 Tu1 Te5 water 5 55 9
## 1156 5 2 Tu1 Te5 water 6 169 59
## 1157 5 2 Tu1 Te5 water 7 196 160
## 1158 5 2 Tu1 Te5 water 8 0 0
## 1159 5 2 Tu1 Te5 water 9 96 52
## 1160 5 2 Tu1 Te5 water 10 178 29
## 1161 5 2 Tu2 Te4 Cd 1 0 0
## 1162 5 2 Tu2 Te4 Cd 2 30 0
## 1163 5 2 Tu2 Te4 Cd 3 12 0
## 1164 5 2 Tu2 Te4 Cd 4 11 0
## 1165 5 2 Tu2 Te4 Cd 5 14 0
## 1166 5 2 Tu2 Te4 Cd 6 2 0
## 1167 5 2 Tu2 Te4 Cd 7 6 8
## 1168 5 2 Tu2 Te4 Cd 8 26 1
## 1169 5 2 Tu2 Te4 Cd 9 63 5
## 1170 5 2 Tu2 Te4 Cd 10 30 0
## 1171 5 2 Tu2 Te4 water 1 49 36
## 1172 5 2 Tu2 Te4 water 2 52 27
## 1173 5 2 Tu2 Te4 water 3 44 43
## 1174 5 2 Tu2 Te4 water 4 1 1
## 1175 5 2 Tu2 Te4 water 5 42 34
## 1176 5 2 Tu2 Te4 water 6 237 17
## 1177 5 2 Tu2 Te4 water 7 67 23
## 1178 5 2 Tu2 Te4 water 8 0 0
## 1179 5 2 Tu2 Te4 water 9 249 27
## 1180 5 2 Tu2 Te4 water 10 169 10
## 1181 5 2 Tu2 Te5 Cd 1 4 0
## 1182 5 2 Tu2 Te5 Cd 2 7 0
## 1183 5 2 Tu2 Te5 Cd 3 34 0
## 1184 5 2 Tu2 Te5 Cd 4 10 0
## 1185 5 2 Tu2 Te5 Cd 5 10 0
## 1186 5 2 Tu2 Te5 Cd 6 21 4
## 1187 5 2 Tu2 Te5 Cd 7 16 2
## 1188 5 2 Tu2 Te5 Cd 8 124 1
## 1189 5 2 Tu2 Te5 Cd 9 103 6
## 1190 5 2 Tu2 Te5 Cd 10 38 1
## 1191 5 2 Tu2 Te5 water 1 0 0
## 1192 5 2 Tu2 Te5 water 2 89 34
## 1193 5 2 Tu2 Te5 water 3 23 1
## 1194 5 2 Tu2 Te5 water 4 37 11
## 1195 5 2 Tu2 Te5 water 5 85 8
## 1196 5 2 Tu2 Te5 water 6 44 0
## 1197 5 2 Tu2 Te5 water 7 121 10
## 1198 5 2 Tu2 Te5 water 8 113 31
## 1199 5 2 Tu2 Te5 water 9 364 32
## 1200 5 2 Tu2 Te5 water 10 41 60
## 1201 5 3 Tu1 Te4 Cd 1 16 0
## 1202 5 3 Tu1 Te4 Cd 2 7 0
## 1203 5 3 Tu1 Te4 Cd 3 23 0
## 1204 5 3 Tu1 Te4 Cd 4 7 0
## 1205 5 3 Tu1 Te4 Cd 5 1 0
## 1206 5 3 Tu1 Te4 Cd 6 26 3
## 1207 5 3 Tu1 Te4 Cd 7 8 1
## 1208 5 3 Tu1 Te4 Cd 8 22 5
## 1209 5 3 Tu1 Te4 Cd 9 59 4
## 1210 5 3 Tu1 Te4 Cd 10 4 2
## 1211 5 3 Tu1 Te4 water 1 66 58
## 1212 5 3 Tu1 Te4 water 2 8 18
## 1213 5 3 Tu1 Te4 water 3 14 32
## 1214 5 3 Tu1 Te4 water 4 3 5
## 1215 5 3 Tu1 Te4 water 5 23 29
## 1216 5 3 Tu1 Te4 water 6 0 0
## 1217 5 3 Tu1 Te4 water 7 0 0
## 1218 5 3 Tu1 Te4 water 8 0 0
## 1219 5 3 Tu1 Te4 water 9 0 0
## 1220 5 3 Tu1 Te4 water 10 0 0
## 1221 5 3 Tu1 Te5 Cd 1 34 0
## 1222 5 3 Tu1 Te5 Cd 2 46 0
## 1223 5 3 Tu1 Te5 Cd 3 30 0
## 1224 5 3 Tu1 Te5 Cd 4 20 0
## 1225 5 3 Tu1 Te5 Cd 5 56 0
## 1226 5 3 Tu1 Te5 Cd 6 28 0
## 1227 5 3 Tu1 Te5 Cd 7 9 2
## 1228 5 3 Tu1 Te5 Cd 8 70 0
## 1229 5 3 Tu1 Te5 Cd 9 58 0
## 1230 5 3 Tu1 Te5 Cd 10 41 1
## 1231 5 3 Tu1 Te5 water 1 3 2
## 1232 5 3 Tu1 Te5 water 2 80 29
## 1233 5 3 Tu1 Te5 water 3 10 4
## 1234 5 3 Tu1 Te5 water 4 71 21
## 1235 5 3 Tu1 Te5 water 5 6 0
## 1236 5 3 Tu1 Te5 water 6 152 124
## 1237 5 3 Tu1 Te5 water 7 0 0
## 1238 5 3 Tu1 Te5 water 8 2 1
## 1239 5 3 Tu1 Te5 water 9 54 15
## 1240 5 3 Tu1 Te5 water 10 93 18
## 1241 5 3 Tu2 Te4 Cd 1 12 0
## 1242 5 3 Tu2 Te4 Cd 2 10 0
## 1243 5 3 Tu2 Te4 Cd 3 24 0
## 1244 5 3 Tu2 Te4 Cd 4 1 0
## 1245 5 3 Tu2 Te4 Cd 5 6 0
## 1246 5 3 Tu2 Te4 Cd 6 20 0
## 1247 5 3 Tu2 Te4 Cd 7 5 5
## 1248 5 3 Tu2 Te4 Cd 8 0 0
## 1249 5 3 Tu2 Te4 Cd 9 20 3
## 1250 5 3 Tu2 Te4 Cd 10 72 5
## 1251 5 3 Tu2 Te4 water 1 32 37
## 1252 5 3 Tu2 Te4 water 2 7 15
## 1253 5 3 Tu2 Te4 water 3 27 20
## 1254 5 3 Tu2 Te4 water 4 5 5
## 1255 5 3 Tu2 Te4 water 5 35 18
## 1256 5 3 Tu2 Te4 water 6 0 0
## 1257 5 3 Tu2 Te4 water 7 214 29
## 1258 5 3 Tu2 Te4 water 8 7 1
## 1259 5 3 Tu2 Te4 water 9 54 9
## 1260 5 3 Tu2 Te4 water 10 142 19
## 1261 5 3 Tu2 Te5 Cd 1 23 0
## 1262 5 3 Tu2 Te5 Cd 2 13 0
## 1263 5 3 Tu2 Te5 Cd 3 11 0
## 1264 5 3 Tu2 Te5 Cd 4 28 0
## 1265 5 3 Tu2 Te5 Cd 5 16 0
## 1266 5 3 Tu2 Te5 Cd 6 0 0
## 1267 5 3 Tu2 Te5 Cd 7 55 2
## 1268 5 3 Tu2 Te5 Cd 8 0 0
## 1269 5 3 Tu2 Te5 Cd 9 3 0
## 1270 5 3 Tu2 Te5 Cd 10 0 0
## 1271 5 3 Tu2 Te5 water 1 55 23
## 1272 5 3 Tu2 Te5 water 2 0 0
## 1273 5 3 Tu2 Te5 water 3 17 29
## 1274 5 3 Tu2 Te5 water 4 80 32
## 1275 5 3 Tu2 Te5 water 5 0 0
## 1276 5 3 Tu2 Te5 water 6 1 1
## 1277 5 3 Tu2 Te5 water 7 103 29
## 1278 5 3 Tu2 Te5 water 8 114 0
## 1279 5 3 Tu2 Te5 water 9 101 7
## 1280 5 3 Tu2 Te5 water 10 51 10
## 1281 5 4 Tu1 Te4 Cd 1 37 0
## 1282 5 4 Tu1 Te4 Cd 2 32 0
## 1283 5 4 Tu1 Te4 Cd 3 72 0
## 1284 5 4 Tu1 Te4 Cd 4 2 0
## 1285 5 4 Tu1 Te4 Cd 5 8 0
## 1286 5 4 Tu1 Te4 Cd 6 16 5
## 1287 5 4 Tu1 Te4 Cd 7 26 4
## 1288 5 4 Tu1 Te4 Cd 8 105 4
## 1289 5 4 Tu1 Te4 Cd 9 121 2
## 1290 5 4 Tu1 Te4 Cd 10 19 8
## 1291 5 4 Tu1 Te4 water 1 49 22
## 1292 5 4 Tu1 Te4 water 2 20 8
## 1293 5 4 Tu1 Te4 water 3 1 3
## 1294 5 4 Tu1 Te4 water 4 0 2
## 1295 5 4 Tu1 Te4 water 5 113 22
## 1296 5 4 Tu1 Te4 water 6 0 0
## 1297 5 4 Tu1 Te4 water 7 239 174
## 1298 5 4 Tu1 Te4 water 8 179 11
## 1299 5 4 Tu1 Te4 water 9 66 2
## 1300 5 4 Tu1 Te4 water 10 83 35
## 1301 5 4 Tu1 Te5 Cd 1 122 1
## 1302 5 4 Tu1 Te5 Cd 2 78 25
## 1303 5 4 Tu1 Te5 Cd 3 60 15
## 1304 5 4 Tu1 Te5 Cd 4 109 2
## 1305 5 4 Tu1 Te5 Cd 5 65 7
## 1306 5 4 Tu1 Te5 Cd 6 117 0
## 1307 5 4 Tu1 Te5 Cd 7 26 1
## 1308 5 4 Tu1 Te5 Cd 8 7 0
## 1309 5 4 Tu1 Te5 Cd 9 22 0
## 1310 5 4 Tu1 Te5 Cd 10 43 4
## 1311 5 4 Tu1 Te5 water 1 108 10
## 1312 5 4 Tu1 Te5 water 2 104 45
## 1313 5 4 Tu1 Te5 water 3 40 51
## 1314 5 4 Tu1 Te5 water 4 44 5
## 1315 5 4 Tu1 Te5 water 5 168 56
## 1316 5 4 Tu1 Te5 water 6 72 51
## 1317 5 4 Tu1 Te5 water 7 0 1
## 1318 5 4 Tu1 Te5 water 8 64 33
## 1319 5 4 Tu1 Te5 water 9 0 0
## 1320 5 4 Tu1 Te5 water 10 0 0
## 1321 5 4 Tu2 Te4 Cd 1 11 0
## 1322 5 4 Tu2 Te4 Cd 2 27 3
## 1323 5 4 Tu2 Te4 Cd 3 21 0
## 1324 5 4 Tu2 Te4 Cd 4 43 0
## 1325 5 4 Tu2 Te4 Cd 5 5 0
## 1326 5 4 Tu2 Te4 Cd 6 40 0
## 1327 5 4 Tu2 Te4 Cd 7 9 1
## 1328 5 4 Tu2 Te4 Cd 8 35 6
## 1329 5 4 Tu2 Te4 Cd 9 23 1
## 1330 5 4 Tu2 Te4 Cd 10 33 1
## 1331 5 4 Tu2 Te4 water 1 137 97
## 1332 5 4 Tu2 Te4 water 2 23 2
## 1333 5 4 Tu2 Te4 water 3 185 11
## 1334 5 4 Tu2 Te4 water 4 4 9
## 1335 5 4 Tu2 Te4 water 5 31 3
## 1336 5 4 Tu2 Te4 water 6 18 0
## 1337 5 4 Tu2 Te4 water 7 151 0
## 1338 5 4 Tu2 Te4 water 8 7 1
## 1339 5 4 Tu2 Te4 water 9 77 5
## 1340 5 4 Tu2 Te4 water 10 180 31
## 1341 5 4 Tu2 Te5 Cd 1 20 0
## 1342 5 4 Tu2 Te5 Cd 2 12 0
## 1343 5 4 Tu2 Te5 Cd 3 12 0
## 1344 5 4 Tu2 Te5 Cd 4 17 0
## 1345 5 4 Tu2 Te5 Cd 5 8 1
## 1346 5 4 Tu2 Te5 Cd 6 169 11
## 1347 5 4 Tu2 Te5 Cd 7 61 4
## 1348 5 4 Tu2 Te5 Cd 8 173 5
## 1349 5 4 Tu2 Te5 Cd 9 3 0
## 1350 5 4 Tu2 Te5 Cd 10 114 1
## 1351 5 4 Tu2 Te5 water 1 3 4
## 1352 5 4 Tu2 Te5 water 2 0 0
## 1353 5 4 Tu2 Te5 water 3 0 0
## 1354 5 4 Tu2 Te5 water 4 1 0
## 1355 5 4 Tu2 Te5 water 5 42 8
## 1356 5 4 Tu2 Te5 water 6 15 0
## 1357 5 4 Tu2 Te5 water 7 198 0
## 1358 5 4 Tu2 Te5 water 8 263 49
## 1359 5 4 Tu2 Te5 water 9 89 27
## 1360 5 4 Tu2 Te5 water 10 24 19
## 1361 5 5 Tu1 Te4 Cd 1 43 0
## 1362 5 5 Tu1 Te4 Cd 2 14 0
## 1363 5 5 Tu1 Te4 Cd 3 23 0
## 1364 5 5 Tu1 Te4 Cd 4 27 1
## 1365 5 5 Tu1 Te4 Cd 5 0 0
## 1366 5 5 Tu1 Te4 Cd 6 133 17
## 1367 5 5 Tu1 Te4 Cd 7 49 1
## 1368 5 5 Tu1 Te4 Cd 8 299 8
## 1369 5 5 Tu1 Te4 Cd 9 136 7
## 1370 5 5 Tu1 Te4 Cd 10 180 13
## 1371 5 5 Tu1 Te4 water 1 129 32
## 1372 5 5 Tu1 Te4 water 2 132 149
## 1373 5 5 Tu1 Te4 water 3 47 55
## 1374 5 5 Tu1 Te4 water 4 126 74
## 1375 5 5 Tu1 Te4 water 5 106 43
## 1376 5 5 Tu1 Te4 water 6 76 0
## 1377 5 5 Tu1 Te4 water 7 482 43
## 1378 5 5 Tu1 Te4 water 8 149 30
## 1379 5 5 Tu1 Te4 water 9 213 16
## 1380 5 5 Tu1 Te4 water 10 176 15
## 1381 5 5 Tu1 Te5 Cd 1 337 0
## 1382 5 5 Tu1 Te5 Cd 2 231 0
## 1383 5 5 Tu1 Te5 Cd 3 72 13
## 1384 5 5 Tu1 Te5 Cd 4 255 3
## 1385 5 5 Tu1 Te5 Cd 5 448 2
## 1386 5 5 Tu1 Te5 Cd 6 132 5
## 1387 5 5 Tu1 Te5 Cd 7 189 13
## 1388 5 5 Tu1 Te5 Cd 8 308 4
## 1389 5 5 Tu1 Te5 Cd 9 125 9
## 1390 5 5 Tu1 Te5 Cd 10 138 23
## 1391 5 5 Tu1 Te5 water 1 152 45
## 1392 5 5 Tu1 Te5 water 2 200 110
## 1393 5 5 Tu1 Te5 water 3 195 160
## 1394 5 5 Tu1 Te5 water 4 179 43
## 1395 5 5 Tu1 Te5 water 5 276 59
## 1396 5 5 Tu1 Te5 water 6 436 201
## 1397 5 5 Tu1 Te5 water 7 230 16
## 1398 5 5 Tu1 Te5 water 8 521 48
## 1399 5 5 Tu1 Te5 water 9 85 19
## 1400 5 5 Tu1 Te5 water 10 184 33
## 1401 5 5 Tu2 Te4 Cd 1 24 0
## 1402 5 5 Tu2 Te4 Cd 2 82 2
## 1403 5 5 Tu2 Te4 Cd 3 36 0
## 1404 5 5 Tu2 Te4 Cd 4 88 0
## 1405 5 5 Tu2 Te4 Cd 5 10 0
## 1406 5 5 Tu2 Te4 Cd 6 3 1
## 1407 5 5 Tu2 Te4 Cd 7 9 10
## 1408 5 5 Tu2 Te4 Cd 8 33 0
## 1409 5 5 Tu2 Te4 Cd 9 28 8
## 1410 5 5 Tu2 Te4 Cd 10 34 9
## 1411 5 5 Tu2 Te4 water 1 157 58
## 1412 5 5 Tu2 Te4 water 2 261 26
## 1413 5 5 Tu2 Te4 water 3 199 52
## 1414 5 5 Tu2 Te4 water 4 96 76
## 1415 5 5 Tu2 Te4 water 5 254 29
## 1416 5 5 Tu2 Te4 water 6 550 39
## 1417 5 5 Tu2 Te4 water 7 561 12
## 1418 5 5 Tu2 Te4 water 8 631 9
## 1419 5 5 Tu2 Te4 water 9 230 13
## 1420 5 5 Tu2 Te4 water 10 157 6
## 1421 5 5 Tu2 Te5 Cd 1 48 5
## 1422 5 5 Tu2 Te5 Cd 2 25 0
## 1423 5 5 Tu2 Te5 Cd 3 6 5
## 1424 5 5 Tu2 Te5 Cd 4 24 1
## 1425 5 5 Tu2 Te5 Cd 5 30 3
## 1426 5 5 Tu2 Te5 Cd 6 207 16
## 1427 5 5 Tu2 Te5 Cd 7 387 11
## 1428 5 5 Tu2 Te5 Cd 8 438 0
## 1429 5 5 Tu2 Te5 Cd 9 431 6
## 1430 5 5 Tu2 Te5 Cd 10 221 0
## 1431 5 5 Tu2 Te5 water 1 148 78
## 1432 5 5 Tu2 Te5 water 2 184 54
## 1433 5 5 Tu2 Te5 water 3 162 37
## 1434 5 5 Tu2 Te5 water 4 513 66
## 1435 5 5 Tu2 Te5 water 5 270 68
## 1436 5 5 Tu2 Te5 water 6 384 52
## 1437 5 5 Tu2 Te5 water 7 514 6
## 1438 5 5 Tu2 Te5 water 8 387 53
## 1439 5 5 Tu2 Te5 water 9 388 19
## 1440 5 5 Tu2 Te5 water 10 446 104coex_g42_rep$Te_ratio<-sapply(c(1:dim(coex_g42_rep)[1]), function(x) coex_g42_rep$sum_Te[x]/sum(coex_g42_rep$sum_Tu[x],coex_g42_rep$sum_Te[x]))
#All the NAN were caused by division by 0, so we put it as 0
coex_g42_rep$Te_ratio[which(coex_g42_rep$Te_ratio=="NaN")]<-0
coex_g42_rep2<-coex_g42_rep %>%
group_by(SRTu, SRTe, Rep2, Box2, Env) %>%
summarize(sumTe=sum(sum_Te, na.rm=TRUE), sumTu=sum(sum_Tu, na.rm=TRUE))## `summarise()` has grouped output by 'SRTu', 'SRTe', 'Rep2', 'Box2'. You can
## override using the `.groups` argument.coex_g42_rep2$Te_ratio<-sapply(c(1:dim(coex_g42_rep2)[1]), function(x) coex_g42_rep2$sumTe[x]/sum(coex_g42_rep2$sumTu[x],coex_g42_rep2$sumTe[x]))
coex_g42_rep3<-coex_g42_rep2 %>%
group_by(SRTu, SRTe, Rep2, Env) %>%
summarize(sum_Te=sum(sumTe, na.rm=TRUE), sum_Tu=sum(sumTu, na.rm=TRUE), sdTe=sd(sumTe, na.rm=TRUE), sdTu=sd(sumTu, na.rm=TRUE), meanTeRatio=mean(Te_ratio, na.rm=TRUE))## `summarise()` has grouped output by 'SRTu', 'SRTe', 'Rep2'. You can override
## using the `.groups` argument.coex_g42_rep3$Te_ratio<-sapply(c(1:dim(coex_g42_rep3)[1]), function(x) coex_g42_rep3$sum_Te[x]/sum(coex_g42_rep3$sum_Tu[x],coex_g42_rep3$sum_Te[x]))summarizing data
str(coex_g42_rep3)## gropd_df [36 × 10] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ Env : chr [1:36] "Cd" "water" "Cd" "water" ...
## $ sum_Te : int [1:36] 1436 1574 1135 624 2862 2487 2745 4329 1806 3285 ...
## $ sum_Tu : num [1:36] 95 909 1 486 129 ...
## $ sdTe : num [1:36] 117 92.7 87.2 73.9 185.5 ...
## $ sdTu : num [1:36] 15.565 55.419 0.316 64.907 26.396 ...
## $ meanTeRatio: num [1:36] 0.958 0.624 0.999 0.648 0.938 ...
## $ Te_ratio : num [1:36] 0.938 0.634 0.999 0.562 0.957 ...
## - attr(*, "groups")= tibble [18 × 4] (S3: tbl_df/tbl/data.frame)
## ..$ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 2 2 2 2 2 ...
## ..$ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## ..$ .rows: list<int> [1:18]
## .. ..$ : int [1:2] 1 2
## .. ..$ : int [1:2] 3 4
## .. ..$ : int [1:2] 5 6
## .. ..$ : int [1:2] 7 8
## .. ..$ : int [1:2] 9 10
## .. ..$ : int [1:2] 11 12
## .. ..$ : int [1:2] 13 14
## .. ..$ : int [1:2] 15 16
## .. ..$ : int [1:2] 17 18
## .. ..$ : int [1:2] 19 20
## .. ..$ : int [1:2] 21 22
## .. ..$ : int [1:2] 23 24
## .. ..$ : int [1:2] 25 26
## .. ..$ : int [1:2] 27 28
## .. ..$ : int [1:2] 29 30
## .. ..$ : int [1:2] 31 32
## .. ..$ : int [1:2] 33 34
## .. ..$ : int [1:2] 35 36
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUEcoex_g42_rep3$Env<-plyr::mapvalues(coex_g42_rep3$Env, c("Cd","water"), c("Cd", "N"))
coex_g42_rep3$SRTu2<-plyr::mapvalues(coex_g42_rep3$SRTu, c("Tu1","Tu2"), c("SR1", "SR2"))
coex_g42_rep3$SRTe2<-plyr::mapvalues(coex_g42_rep3$SRTe, c("Te4","Te5"), c("SR4", "SR5"))
sum_observed_coex<-coex_g42_rep3
str(sum_observed_coex)## gropd_df [36 × 12] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ Env : chr [1:36] "Cd" "N" "Cd" "N" ...
## $ sum_Te : int [1:36] 1436 1574 1135 624 2862 2487 2745 4329 1806 3285 ...
## $ sum_Tu : num [1:36] 95 909 1 486 129 ...
## $ sdTe : num [1:36] 117 92.7 87.2 73.9 185.5 ...
## $ sdTu : num [1:36] 15.565 55.419 0.316 64.907 26.396 ...
## $ meanTeRatio: num [1:36] 0.958 0.624 0.999 0.648 0.938 ...
## $ Te_ratio : num [1:36] 0.938 0.634 0.999 0.562 0.957 ...
## $ SRTu2 : Factor w/ 2 levels "SR1","SR2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe2 : Factor w/ 2 levels "SR4","SR5": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "groups")= tibble [18 × 4] (S3: tbl_df/tbl/data.frame)
## ..$ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 2 2 2 2 2 ...
## ..$ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## ..$ .rows: list<int> [1:18]
## .. ..$ : int [1:2] 1 2
## .. ..$ : int [1:2] 3 4
## .. ..$ : int [1:2] 5 6
## .. ..$ : int [1:2] 7 8
## .. ..$ : int [1:2] 9 10
## .. ..$ : int [1:2] 11 12
## .. ..$ : int [1:2] 13 14
## .. ..$ : int [1:2] 15 16
## .. ..$ : int [1:2] 17 18
## .. ..$ : int [1:2] 19 20
## .. ..$ : int [1:2] 21 22
## .. ..$ : int [1:2] 23 24
## .. ..$ : int [1:2] 25 26
## .. ..$ : int [1:2] 27 28
## .. ..$ : int [1:2] 29 30
## .. ..$ : int [1:2] 31 32
## .. ..$ : int [1:2] 33 34
## .. ..$ : int [1:2] 35 36
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUEwrite.csv(sum_observed_coex, "./TableS3.csv")Comparing to data
sum_observed_coex2<-sum_observed_coex %>%
group_by(SRTu2, SRTe2, Env)%>%
summarise(sumTe=sum(sum_Te, na.rm=TRUE),sumTu=mean(sum_Tu, na.rm=TRUE), sdTe2=sd(sum_Te, na.rm=TRUE)/sqrt(5), sdTu2=sd(sum_Tu, na.rm=TRUE)/sqrt(5), TeRatio=mean(meanTeRatio, na.rm=TRUE), sdTeRatio2=sd(meanTeRatio, na.rm=TRUE)/sqrt(5)) %>% as.data.frame()## `summarise()` has grouped output by 'SRTu2', 'SRTe2'. You can override using
## the `.groups` argument.sum_observed_coex2$TeRatio_L<-sum_observed_coex2$TeRatio-sum_observed_coex2$sdTeRatio2
sum_observed_coex2$TeRatio_U<-sum_observed_coex2$TeRatio+sum_observed_coex2$sdTeRatio2¢## Testing predictions proportions
str(pred_coex_RK_REP)## 'data.frame': 8 obs. of 15 variables:
## $ SRTe : chr "SR4" "SR4" "SR4" "SR4" ...
## $ SRTu : chr "SR1" "SR1" "SR2" "SR2" ...
## $ Env : chr "N" "Cd" "N" "Cd" ...
## $ predTu1 : num 9.86 6.47 10.13 7.15 10.44 ...
## $ predTe1 : num 17.8 10.6 21.8 10.4 16.5 ...
## $ predTu2 : num 9.05 6.59 7.91 7.88 11.64 ...
## $ predTe2 : num 30.3 18 47.1 17.2 30.5 ...
## $ predTu1_L: num 10.69 6.7 11.15 7.53 11.28 ...
## $ predTe1_L: num 20.6 11.6 25.8 11.3 19.1 ...
## $ predTu2_L: num 11.9 7.25 11.38 9.1 15.41 ...
## $ predTe2_L: num 57.1 22.6 107.6 22 56.8 ...
## $ predTu1_U: num 9.05 6.24 9.15 6.77 9.62 ...
## $ predTe1_U: num 15.13 9.73 18.13 9.45 14.09 ...
## $ predTu2_U: num 7.42 6.06 6.16 6.95 9.42 ...
## $ predTe2_U: num 18.5 14.6 25.2 13.8 18.5 ...pred_coex_RK_REP$TeRatio<-sapply(c(1:dim(pred_coex_RK_REP)[1]), function(x){
pred_coex_RK_REP$predTe2[x]/(pred_coex_RK_REP$predTe2[x]+pred_coex_RK_REP$predTu2[x])
})
pred_coex_RK_w0$TeRatio<-sapply(c(1:dim(pred_coex_RK_w0)[1]), function(x){
pred_coex_RK_w0$predTe2[x]/(pred_coex_RK_w0$predTe2[x]+pred_coex_RK_w0$predTu2[x])
})
pred_coex_RK_REP$TeRatio_L<-sapply(c(1:dim(pred_coex_RK_REP)[1]), function(x){
pred_coex_RK_REP$predTe2_L[x]/(pred_coex_RK_REP$predTe2_L[x]+pred_coex_RK_REP$predTu2_L[x])
})
pred_coex_RK_w0$TeRatio_L<-sapply(c(1:dim(pred_coex_RK_w0)[1]), function(x){
pred_coex_RK_w0$predTe2_L[x]/(pred_coex_RK_w0$predTe2_L[x]+pred_coex_RK_w0$predTu2_L[x])
})
pred_coex_RK_REP$TeRatio_U<-sapply(c(1:dim(pred_coex_RK_REP)[1]), function(x){
pred_coex_RK_REP$predTe2_U[x]/(pred_coex_RK_REP$predTe2_U[x]+pred_coex_RK_REP$predTu2_U[x])
})
pred_coex_RK_w0$TeRatio_U<-sapply(c(1:dim(pred_coex_RK_w0)[1]), function(x){
pred_coex_RK_w0$predTe2_U[x]/(pred_coex_RK_w0$predTe2_U[x]+pred_coex_RK_w0$predTu2_U[x])
})
str(pred_coex_RK_REP)## 'data.frame': 8 obs. of 18 variables:
## $ SRTe : chr "SR4" "SR4" "SR4" "SR4" ...
## $ SRTu : chr "SR1" "SR1" "SR2" "SR2" ...
## $ Env : chr "N" "Cd" "N" "Cd" ...
## $ predTu1 : num 9.86 6.47 10.13 7.15 10.44 ...
## $ predTe1 : num 17.8 10.6 21.8 10.4 16.5 ...
## $ predTu2 : num 9.05 6.59 7.91 7.88 11.64 ...
## $ predTe2 : num 30.3 18 47.1 17.2 30.5 ...
## $ predTu1_L: num 10.69 6.7 11.15 7.53 11.28 ...
## $ predTe1_L: num 20.6 11.6 25.8 11.3 19.1 ...
## $ predTu2_L: num 11.9 7.25 11.38 9.1 15.41 ...
## $ predTe2_L: num 57.1 22.6 107.6 22 56.8 ...
## $ predTu1_U: num 9.05 6.24 9.15 6.77 9.62 ...
## $ predTe1_U: num 15.13 9.73 18.13 9.45 14.09 ...
## $ predTu2_U: num 7.42 6.06 6.16 6.95 9.42 ...
## $ predTe2_U: num 18.5 14.6 25.2 13.8 18.5 ...
## $ TeRatio : num 0.77 0.732 0.856 0.686 0.724 ...
## $ TeRatio_L: num 0.828 0.757 0.904 0.707 0.786 ...
## $ TeRatio_U: num 0.713 0.707 0.804 0.665 0.662 ...sum_pred_coex_RK_REP<-pred_coex_RK_REP %>%
group_by(SRTu, SRTe, Env)%>%
summarise(predTe=mean(predTe2, na.rm=TRUE),predTu=mean(predTu2, na.rm=TRUE), sumTeRatio=(sum(predTe2, na.rm=TRUE)/(sum(predTe2, na.rm=TRUE)+sum(predTu2, na.rm=TRUE)))) %>% as.data.frame()## `summarise()` has grouped output by 'SRTu', 'SRTe'. You can override using the
## `.groups` argument.Comparing to proportions
str(sum_observed_coex)## gropd_df [36 × 12] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 1 1 1 1 1 ...
## $ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ Env : chr [1:36] "Cd" "N" "Cd" "N" ...
## $ sum_Te : int [1:36] 1436 1574 1135 624 2862 2487 2745 4329 1806 3285 ...
## $ sum_Tu : num [1:36] 95 909 1 486 129 ...
## $ sdTe : num [1:36] 117 92.7 87.2 73.9 185.5 ...
## $ sdTu : num [1:36] 15.565 55.419 0.316 64.907 26.396 ...
## $ meanTeRatio: num [1:36] 0.958 0.624 0.999 0.648 0.938 ...
## $ Te_ratio : num [1:36] 0.938 0.634 0.999 0.562 0.957 ...
## $ SRTu2 : Factor w/ 2 levels "SR1","SR2": 1 1 1 1 1 1 1 1 1 1 ...
## $ SRTe2 : Factor w/ 2 levels "SR4","SR5": 1 1 1 1 1 1 1 1 1 1 ...
## - attr(*, "groups")= tibble [18 × 4] (S3: tbl_df/tbl/data.frame)
## ..$ SRTu : Factor w/ 2 levels "Tu1","Tu2": 1 1 1 1 1 1 1 1 1 1 ...
## ..$ SRTe : Factor w/ 2 levels "Te4","Te5": 1 1 1 1 1 2 2 2 2 2 ...
## ..$ Rep2 : Factor w/ 5 levels "1","2","3","4",..: 1 2 3 4 5 1 2 3 4 5 ...
## ..$ .rows: list<int> [1:18]
## .. ..$ : int [1:2] 1 2
## .. ..$ : int [1:2] 3 4
## .. ..$ : int [1:2] 5 6
## .. ..$ : int [1:2] 7 8
## .. ..$ : int [1:2] 9 10
## .. ..$ : int [1:2] 11 12
## .. ..$ : int [1:2] 13 14
## .. ..$ : int [1:2] 15 16
## .. ..$ : int [1:2] 17 18
## .. ..$ : int [1:2] 19 20
## .. ..$ : int [1:2] 21 22
## .. ..$ : int [1:2] 23 24
## .. ..$ : int [1:2] 25 26
## .. ..$ : int [1:2] 27 28
## .. ..$ : int [1:2] 29 30
## .. ..$ : int [1:2] 31 32
## .. ..$ : int [1:2] 33 34
## .. ..$ : int [1:2] 35 36
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUEsum_observed_coex_rep2<-sum_observed_coex %>%
group_by(SRTe, SRTu, Env) %>%
summarize(obs_TeRatio=mean(meanTeRatio), SE_obs=sd(meanTeRatio)/sqrt(5), meanTe=mean(sum_Te, na.rm=TRUE), meanTu=mean(sum_Tu, na.rm=TRUE)) %>% as.data.frame()## `summarise()` has grouped output by 'SRTe', 'SRTu'. You can override using the
## `.groups` argument.sum_observed_coex_rep2$SRTe2<-(plyr::mapvalues(as.character(sum_observed_coex_rep2$SRTe), c("Te4","Te5"), c("SR4", "SR5")))
sum_observed_coex_rep2$SRTu2<-(plyr::mapvalues(as.character(sum_observed_coex_rep2$SRTu), c("Tu1","Tu2"), c("SR1", "SR2")))
colnames(sum_observed_coex_rep2)[c(1:2, 8,9)]<-c("SRTe2", "SRTu2","SRTe", "SRTu" )
sum_observed_coex_rep2<-sum_observed_coex_rep2[,c(8,9,3:7)]
colnames(sum_observed_coex)[c(1,2,3,11:12)]<-c("SRTu2","SRTe2","Replicate","SRTu","SRTe")
sum_observed_coex_rep<-inner_join(sum_observed_coex_rep2, pred_coex_RK_REP, by=c("SRTe", "SRTu", "Env"))
sum_observed_coex_rep<-as.data.frame(sum_observed_coex_rep[,c("SRTe", "SRTu", "Env", "obs_TeRatio","SE_obs", "TeRatio", "TeRatio_L", "TeRatio_U", "meanTe","meanTu", "predTu2","predTe2")])
str(sum_observed_coex_rep)## 'data.frame': 8 obs. of 12 variables:
## $ SRTe : chr "SR4" "SR4" "SR4" "SR4" ...
## $ SRTu : chr "SR1" "SR1" "SR2" "SR2" ...
## $ Env : chr "Cd" "N" "Cd" "N" ...
## $ obs_TeRatio: num 0.963 0.707 0.894 0.737 0.956 ...
## $ SE_obs : num 0.01 0.0308 0.0222 0.0378 0.0111 ...
## $ TeRatio : num 0.732 0.77 0.686 0.856 0.569 ...
## $ TeRatio_L : num 0.757 0.828 0.707 0.904 0.587 ...
## $ TeRatio_U : num 0.707 0.713 0.665 0.804 0.552 ...
## $ meanTe : num 1997 2460 1603 3022 2881 ...
## $ meanTu : num 88.6 1088.6 198 810.5 109.8 ...
## $ predTu2 : num 6.59 9.05 7.88 7.91 7.99 ...
## $ predTe2 : num 18 30.3 17.2 47.1 10.6 ...colnames(sum_observed_coex_rep)[6:8]<-c("pred_T1", "T1_L","T1_U")
pred_coex_RK_w0$Replicate<-as.factor(pred_coex_RK_w0$Replicate)
sum_observed_coex_ALL<-inner_join(sum_observed_coex, pred_coex_RK_w0, by=c("SRTe", "SRTu", "Env", "Replicate"))
sum2_observed_coex_ALL<-as.data.frame(sum_observed_coex_ALL[,c(11,12,3,4,5,6,9,15,16)])
str(sum2_observed_coex_ALL)## 'data.frame': 36 obs. of 9 variables:
## $ SRTu : chr "SR1" "SR1" "SR1" "SR1" ...
## $ SRTe : chr "SR4" "SR4" "SR4" "SR4" ...
## $ Replicate : Factor w/ 5 levels "1","2","3","4",..: 1 1 2 2 3 3 4 4 5 5 ...
## $ Env : chr "Cd" "N" "Cd" "N" ...
## $ sum_Te : int 1436 1574 1135 624 2862 2487 2745 4329 1806 3285 ...
## $ sum_Tu : num 95 909 1 486 129 ...
## $ meanTeRatio: num 0.958 0.624 0.999 0.648 0.938 ...
## $ predTu2 : num 5.46 8.02 7.78 22.15 7.93 ...
## $ predTe2 : num 20.19 21.84 22.13 4.57 10.88 ...sum2_observed_coex_ALL$predTeRatio<-sapply(c(1:dim(sum2_observed_coex_ALL)[1]), function(x){sum2_observed_coex_ALL$predTe2[x]/sum(sum2_observed_coex_ALL$predTe2[x],sum2_observed_coex_ALL$predTu2[x])})Testing pooled replicates
m3<- glm(cbind(meanTe, meanTu)~pred_T1, data=sum_observed_coex_rep, family="binomial")## Warning in eval(family$initialize): non-integer counts in a binomial glm!m4<- glm(obs_TeRatio~pred_T1, data=sum_observed_coex_rep, family="binomial")## Warning in eval(family$initialize): non-integer #successes in a binomial glm!summary(m4)##
## Call:
## glm(formula = obs_TeRatio ~ pred_T1, family = "binomial", data = sum_observed_coex_rep)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.702 8.805 0.648 0.517
## pred_T1 -5.727 11.988 -0.478 0.633
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 0.72177 on 7 degrees of freedom
## Residual deviance: 0.48675 on 6 degrees of freedom
## AIC: 7.034
##
## Number of Fisher Scoring iterations: 5summary(m3)##
## Call:
## glm(formula = cbind(meanTe, meanTu) ~ pred_T1, family = "binomial",
## data = sum_observed_coex_rep)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.405 0.159 34.0 <2e-16 ***
## pred_T1 -5.238 0.213 -24.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1980.4 on 7 degrees of freedom
## Residual deviance: 1348.9 on 6 degrees of freedom
## AIC: 1415
##
## Number of Fisher Scoring iterations: 5summary(m4)##
## Call:
## glm(formula = obs_TeRatio ~ pred_T1, family = "binomial", data = sum_observed_coex_rep)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.702 8.805 0.648 0.517
## pred_T1 -5.727 11.988 -0.478 0.633
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 0.72177 on 7 degrees of freedom
## Residual deviance: 0.48675 on 6 degrees of freedom
## AIC: 7.034
##
## Number of Fisher Scoring iterations: 5emtrends(m3, var="pred_T1", type="response")## 'emmGrid' object with variables:
## pred_T1 = 0.70686emtrends(m4, var="pred_T1", type="response")## 'emmGrid' object with variables:
## pred_T1 = 0.70686m5<-glm(cbind(meanTe, meanTu)~0+pred_T1, data=sum_observed_coex_rep, family="binomial")## Warning in eval(family$initialize): non-integer counts in a binomial glm!summary(m5)##
## Call:
## glm(formula = cbind(meanTe, meanTu) ~ 0 + pred_T1, family = "binomial",
## data = sum_observed_coex_rep)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## pred_T1 2.12736 0.02484 85.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 12352 on 8 degrees of freedom
## Residual deviance: 2627 on 7 degrees of freedom
## AIC: 2690
##
## Number of Fisher Scoring iterations: 5emtrends(m5, var="pred_T1", type="response")## 'emmGrid' object with variables:
## pred_T1 = 0.70686sum_observed_coex_rep<-sum_observed_coex_rep %>% mutate(yhat=predict(m5))Testing per replicate
sum_observed_coex_ALL2<-sum2_observed_coex_ALL %>%
group_by(SRTe, SRTu, Env) %>%
summarize(meanRatio=mean(meanTeRatio, na.rm=TRUE), mean_pred=mean(predTeRatio, na.rm=TRUE), sdRatio=sd(meanTeRatio, na.rm=TRUE)/sqrt(5), sdPred=sd(predTeRatio, na.rm=TRUE)/sqrt(5), meanTe=mean(sum_Te, na.rm=TRUE), meanTu=mean(sum_Tu, na.rm=TRUE))## `summarise()` has grouped output by 'SRTe', 'SRTu'. You can override using the
## `.groups` argument.m4<- glmmTMB(cbind(meanTe, meanTu)~mean_pred*Env, data=sum_observed_coex_ALL2, family=binomial(link="logit"))## Warning in eval(family$initialize): non-integer counts in a binomial glm!summary(m4)## Family: binomial ( logit )
## Formula: cbind(meanTe, meanTu) ~ mean_pred * Env
## Data: sum_observed_coex_ALL2
##
## AIC BIC logLik deviance df.resid
## 407.8 408.1 -199.9 399.8 4
##
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.0171 0.4143 9.695 < 2e-16 ***
## mean_pred -1.9980 0.6353 -3.145 0.00166 **
## EnvN -3.7002 0.4296 -8.613 < 2e-16 ***
## mean_pred:EnvN 3.1957 0.6599 4.843 1.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# This is from the summary of the model
slope_all<-invlogit(1.4211)
slope_ci_L<-invlogit(1.4211)-0.1861
slope_ci_U<-invlogit(1.4211)+0.1861
ggplot(sum_observed_coex_ALL2, aes(x=meanRatio,y=mean_pred))+
#geom_smooth(method="glm", colour="black", fullrange=TRUE, family="binomial")+
geom_abline(intercept = 0, slope=slope_all)+
geom_abline(intercept = 0, slope=slope_ci_L, linetype="dashed")+
geom_abline(intercept = 0, slope=slope_ci_U, linetype="dashed")+
#geom_abline(intercept = 0, slope=0.7)+
geom_errorbar(aes(ymin=mean_pred-sdPred, ymax=mean_pred+sdPred), width=0.02, colour="black")+
geom_errorbarh(aes(xmin=meanRatio-sdRatio, xmax=meanRatio+sdRatio), height=0.03, colour="black")+
geom_point(size=3, aes(fill=interaction(SRTu, SRTe), shape=Env))+
scale_fill_manual(values=c("#D7191C", "#FDAE61" ,"#ABDDA4", "#2B83BA"), labels=c("Te no cadmium:Tu no cadmium", "Te cadmium: Tu no cadmium", "Te no cadmium: Tu cadmium", "Te cadmium: Tu cadmium"))+
scale_shape_manual(values=c(22,23))+
theme_ines+
xlab("Observed ratio")+
ylab("Predicted ratio")+
ylim(c(0.3,1))+
xlim(c(0.55,1))+
theme(legend.position = "none")
save_plot("./Analyses/cxr_normal_REP/Fig4.pdf", width=10, height=10)
write.csv(pred_coex_RK_w0, "./PredictedPerReplicate.csv")
write.csv(pred_coex_RK_REP, "./PredictedPooledReplicate.csv")5 - Simulations for figure 1
pred_coex1Gen<-as.data.frame(expand_grid(Te=c("SR4","SR5"), Tu=c("SR1", "SR2"), Environment= c("N", "Cd")))
pred_coex1Gen$predTu_onlyLambda<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*10
bl
})
pred_coex1Gen$predTu_Lambda_INTRA<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10)
bl
})
pred_coex1Gen$predTu_ALL<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10- aux_alphas$Tu_inter[1]*10)
bl
})
pred_coex1Gen$predTe_onlyLambda<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*10
bl
})
pred_coex1Gen$predTe_Lambda_INTRA<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10)
bl
})
pred_coex1Gen$predTe_ALL<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10- aux_alphas$Te_inter[1]*10)
bl
})
pred_coex1Gen$Control_lambdaTu<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Tu=="SR1")
pred_coex1Gen$predTu_onlyLambda[x]/cont$predTu_onlyLambda[1]
})
pred_coex1Gen$Control_lambdaTe<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4")
pred_coex1Gen$predTe_onlyLambda[x]/cont$predTe_onlyLambda[1]
})
pred_coex1Gen$Control_lambdaIntraTu<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_Lambda_INTRA[x]/cont$predTu_Lambda_INTRA[1]
})
pred_coex1Gen$Control_lambdaIntraTe<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_Lambda_INTRA[x]/cont$predTe_Lambda_INTRA[1]
})
pred_coex1Gen$Control_ALLTu<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_ALL[x]/cont$predTu_ALL[1]
})
pred_coex1Gen$Control_ALLTe<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_ALL[x]/cont$predTe_ALL[1]
})Lower
pred_coex1Gen$predTu_onlyLambda_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*10
bl
})
pred_coex1Gen$predTu_Lambda_INTRA_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_lambdas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_lambdas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10)
bl
})
pred_coex1Gen$predTu_ALL_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_lambdas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_lambdas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10- aux_alphas$Tu_inter[1]*10)
bl
})
pred_coex1Gen$predTe_onlyLambda_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*10
bl
})
pred_coex1Gen$predTe_Lambda_INTRA_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_lambdas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_lambdas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10)
bl
})
pred_coex1Gen$predTe_ALL_L<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_lambdas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_lambdas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10- aux_alphas$Te_inter[1]*10)
bl
})
pred_coex1Gen$Control_lambdaTu_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Tu=="SR1")
pred_coex1Gen$predTu_onlyLambda_L[x]/cont$predTu_onlyLambda_L[1]
})
pred_coex1Gen$Control_lambdaTe_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4")
pred_coex1Gen$predTe_onlyLambda_L[x]/cont$predTe_onlyLambda_L[1]
})
pred_coex1Gen$Control_lambdaIntraTu_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_Lambda_INTRA_L[x]/cont$predTu_Lambda_INTRA_L[1]
})
pred_coex1Gen$Control_lambdaIntraTe_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_Lambda_INTRA_L[x]/cont$predTe_Lambda_INTRA_L[1]
})
pred_coex1Gen$Control_ALLTu_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_ALL_L[x]/cont$predTu_ALL_L[1]
})
pred_coex1Gen$Control_ALLTe_L<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_ALL_L[x]/cont$predTe_ALL_L[1]
})Upper
pred_coex1Gen$predTu_onlyLambda_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Tu_lambda[1]*10
bl
})
pred_coex1Gen$predTu_lambda_INTRA_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_Lambdas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_Lambdas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10)
bl
})
pred_coex1Gen$predTu_ALL_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_Lambdas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_Lambdas$Tu_lambda[1]*10*exp(-aux_alphas$Tu_intra[1]*10- aux_alphas$Tu_inter[1]*10)
bl
})
pred_coex1Gen$predTe_onlyLambda_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_alphas$Te_lambda[1]*10
bl
})
pred_coex1Gen$predTe_lambda_INTRA_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_Lambdas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_Lambdas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10)
bl
})
pred_coex1Gen$predTe_ALL_U<-sapply(c(1:length(pred_coex1Gen$Tu)), function(x){
aux_alphas<-subset(param_all_REP_lower, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
aux_Lambdas<-subset(param_all_REP_upper, Tu_Regime==as.character(pred_coex1Gen$Tu[x]) & Te_Regime==as.character(pred_coex1Gen$Te[x]) & Environment==as.character(pred_coex1Gen$Environment[x]))
bl<-aux_Lambdas$Te_lambda[1]*10*exp(-aux_alphas$Te_intra[1]*10- aux_alphas$Te_inter[1]*10)
bl
})
pred_coex1Gen$Control_LambdaTu_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Tu=="SR1")
pred_coex1Gen$predTu_onlyLambda_U[x]/cont$predTu_onlyLambda_U[1]
})
pred_coex1Gen$Control_LambdaTe_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4")
pred_coex1Gen$predTe_onlyLambda_U[x]/cont$predTe_onlyLambda_U[1]
})
pred_coex1Gen$Control_LambdaIntraTu_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_lambda_INTRA_U[x]/cont$predTu_lambda_INTRA_U[1]
})
pred_coex1Gen$Control_LambdaIntraTe_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_lambda_INTRA_U[x]/cont$predTe_lambda_INTRA_U[1]
})
pred_coex1Gen$Control_ALLTu_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTu_ALL_U[x]/cont$predTu_ALL_U[1]
})
pred_coex1Gen$Control_ALLTe_U<-sapply(c(1:dim(pred_coex1Gen)[1]), function(x){
cont<-subset(pred_coex1Gen, Environment==pred_coex1Gen$Environment[x] & Te=="SR4" & Tu=="SR1")
pred_coex1Gen$predTe_ALL_U[x]/cont$predTe_ALL_U[1]
})
pred_coex1Gen[,c("predTu_ALL", "predTu_ALL_U", "predTu_onlyLambda", "predTu_onlyLambda_U", "predTu_Lambda_INTRA", "predTu_lambda_INTRA_U")]## predTu_ALL predTu_ALL_U predTu_onlyLambda predTu_onlyLambda_U
## 1 12.473836 17.13965 24.82408 26.50963
## 2 9.916002 11.48540 12.20772 12.61243
## 3 12.834762 18.32623 25.47805 27.32934
## 4 10.691389 13.13282 14.02913 14.64993
## 5 13.739945 18.76665 24.82408 26.50963
## 6 11.180061 13.07897 12.20772 12.61243
## 7 17.080018 24.17479 25.47805 27.32934
## 8 11.439811 14.30747 14.02913 14.64993
## predTu_Lambda_INTRA predTu_lambda_INTRA_U
## 1 18.57041 22.36856
## 2 11.08162 12.05820
## 3 19.52653 23.98961
## 4 12.36345 13.90353
## 5 18.57041 22.36856
## 6 11.08162 12.05820
## 7 19.52653 23.98961
## 8 12.36345 13.90353reshaping
pred_coex1Gen_long<-gather(pred_coex1Gen[,c("Te","Tu","Environment","Control_lambdaTe" , "Control_lambdaTu","Control_lambdaIntraTe", "Control_lambdaIntraTu","Control_ALLTe" ,"Control_ALLTu", "predTe_onlyLambda","predTe_Lambda_INTRA" ,"predTe_ALL" ,"predTu_onlyLambda","predTu_Lambda_INTRA","predTu_ALL" )], parameter, value, c("Control_lambdaTe" , "Control_lambdaTu","Control_lambdaIntraTe", "Control_lambdaIntraTu","Control_ALLTe" ,"Control_ALLTu", "predTe_onlyLambda","predTe_Lambda_INTRA" ,"predTe_ALL" ,"predTu_onlyLambda","predTu_Lambda_INTRA","predTu_ALL" ))
pred_coex1Gen_long_L<-gather(pred_coex1Gen[,c("Te","Tu","Environment","Control_lambdaTe_L" , "Control_lambdaTu_L","Control_lambdaIntraTe_L", "Control_lambdaIntraTu_L","Control_ALLTe_L" ,"Control_ALLTu_L", "predTe_onlyLambda_L","predTe_Lambda_INTRA_L" ,"predTe_ALL_L" ,"predTu_onlyLambda_L","predTu_Lambda_INTRA_L","predTu_ALL_L" )], parameter, value_L, c("Control_lambdaTe_L" , "Control_lambdaTu_L","Control_lambdaIntraTe_L", "Control_lambdaIntraTu_L","Control_ALLTe_L" ,"Control_ALLTu_L", "predTe_onlyLambda_L","predTe_Lambda_INTRA_L" ,"predTe_ALL_L" ,"predTu_onlyLambda_L","predTu_Lambda_INTRA_L","predTu_ALL_L"))
pred_coex1Gen_long_L$parameter2<-mapvalues(pred_coex1Gen_long_L$parameter,c("Control_lambdaTe_L" , "Control_lambdaTu_L","Control_lambdaIntraTe_L", "Control_lambdaIntraTu_L","Control_ALLTe_L" ,"Control_ALLTu_L", "predTe_onlyLambda_L","predTe_Lambda_INTRA_L" ,"predTe_ALL_L" ,"predTu_onlyLambda_L","predTu_Lambda_INTRA_L","predTu_ALL_L"), c("Control_lambdaTe" , "Control_lambdaTu","Control_lambdaIntraTe", "Control_lambdaIntraTu","Control_ALLTe" ,"Control_ALLTu", "predTe_onlyLambda","predTe_Lambda_INTRA" ,"predTe_ALL" ,"predTu_onlyLambda","predTu_Lambda_INTRA","predTu_ALL") )
pred_coex1Gen_long_U<-gather(pred_coex1Gen[,c("Te","Tu","Environment","Control_LambdaTe_U" , "Control_LambdaTu_U","Control_LambdaIntraTe_U", "Control_LambdaIntraTu_U","Control_ALLTe_U" ,"Control_ALLTu_U", "predTe_onlyLambda_U","predTe_lambda_INTRA_U" ,"predTe_ALL_U" ,"predTu_onlyLambda_U","predTu_lambda_INTRA_U","predTu_ALL_U" )], parameter, value_U, c("Control_LambdaTe_U" , "Control_LambdaTu_U","Control_LambdaIntraTe_U", "Control_LambdaIntraTu_U","Control_ALLTe_U" ,"Control_ALLTu_U", "predTe_onlyLambda_U","predTe_lambda_INTRA_U" ,"predTe_ALL_U" ,"predTu_onlyLambda_U","predTu_lambda_INTRA_U","predTu_ALL_U") )
pred_coex1Gen_long_U$parameter2<-mapvalues(pred_coex1Gen_long_U$parameter,c("Control_LambdaTe_U" , "Control_LambdaTu_U","Control_LambdaIntraTe_U", "Control_LambdaIntraTu_U","Control_ALLTe_U" ,"Control_ALLTu_U", "predTe_onlyLambda_U","predTe_lambda_INTRA_U" ,"predTe_ALL_U" ,"predTu_onlyLambda_U","predTu_lambda_INTRA_U","predTu_ALL_U"), c("Control_lambdaTe" , "Control_lambdaTu","Control_lambdaIntraTe", "Control_lambdaIntraTu","Control_ALLTe" ,"Control_ALLTu", "predTe_onlyLambda","predTe_Lambda_INTRA" ,"predTe_ALL" ,"predTu_onlyLambda","predTu_Lambda_INTRA","predTu_ALL") )
pred_coex1Gen_long$parameter2<-pred_coex1Gen_long$parameter
pred_coex1Gen_long<-left_join(pred_coex1Gen_long, pred_coex1Gen_long_L, by=c("Te","Tu", "parameter2","Environment"))
pred_coex1Gen_long<-left_join(pred_coex1Gen_long, pred_coex1Gen_long_U, by=c("Te","Tu", "parameter2","Environment"))
colnames(pred_coex1Gen_long)<-c("Te", "Tu", "Environment", "parameter", "value", "parameter2","parameter_L", "value_L", "parameter_U", "value_U" )
pred_coex1Gen_long$parameter3<-factor(pred_coex1Gen_long$parameter, c("Control_lambdaTe" , "Control_lambdaTu","Control_lambdaIntraTe", "Control_lambdaIntraTu","Control_ALLTe" ,"Control_ALLTu", "predTe_onlyLambda","predTe_Lambda_INTRA" ,"predTe_ALL" ,"predTu_onlyLambda","predTu_Lambda_INTRA","predTu_ALL"))
str(pred_coex1Gen)## 'data.frame': 8 obs. of 39 variables:
## $ Te : chr "SR4" "SR4" "SR4" "SR4" ...
## $ Tu : chr "SR1" "SR1" "SR2" "SR2" ...
## $ Environment : chr "N" "Cd" "N" "Cd" ...
## $ predTu_onlyLambda : num 24.8 12.2 25.5 14 24.8 ...
## $ predTu_Lambda_INTRA : num 18.6 11.1 19.5 12.4 18.6 ...
## $ predTu_ALL : num 12.47 9.92 12.83 10.69 13.74 ...
## $ predTe_onlyLambda : num 55.4 18 55.4 18 44.3 ...
## $ predTe_Lambda_INTRA : num 45.6 16.3 45.6 16.3 41 ...
## $ predTe_ALL : num 19.6 17.5 27.4 16.8 20 ...
## $ Control_lambdaTu : num 1 1 1.03 1.15 1 ...
## $ Control_lambdaTe : num 1 1 1 1 0.8 ...
## $ Control_lambdaIntraTu : num 1 1 1.05 1.12 1 ...
## $ Control_lambdaIntraTe : num 1 1 1 1 0.899 ...
## $ Control_ALLTu : num 1 1 1.03 1.08 1.1 ...
## $ Control_ALLTe : num 1 1 1.4 0.96 1.02 ...
## $ predTu_onlyLambda_L : num 23.1 11.8 23.6 13.4 23.1 ...
## $ predTu_Lambda_INTRA_L : num 15.3 10.2 15.8 11 15.3 ...
## $ predTu_ALL_L : num 9.04 8.55 8.94 8.69 10.01 ...
## $ predTe_onlyLambda_L : num 49 16.9 49 16.9 39.2 ...
## $ predTe_Lambda_INTRA_L : num 31.4 13.8 31.4 13.8 29.1 ...
## $ predTe_ALL_L : num 11 13 14.9 12.3 11.4 ...
## $ Control_lambdaTu_L : num 1 1 1.02 1.14 1 ...
## $ Control_lambdaTe_L : num 1 1 1 1 0.8 ...
## $ Control_lambdaIntraTu_L: num 1 1 1.03 1.08 1 ...
## $ Control_lambdaIntraTe_L: num 1 1 1 1 0.926 ...
## $ Control_ALLTu_L : num 1 1 0.989 1.016 1.108 ...
## $ Control_ALLTe_L : num 1 1 1.351 0.952 1.033 ...
## $ predTu_onlyLambda_U : num 26.5 12.6 27.3 14.6 26.5 ...
## $ predTu_lambda_INTRA_U : num 22.4 12.1 24 13.9 22.4 ...
## $ predTu_ALL_U : num 17.1 11.5 18.3 13.1 18.8 ...
## $ predTe_onlyLambda_U : num 61.8 19 61.8 19 49.4 ...
## $ predTe_lambda_INTRA_U : num 65.5 19 65.5 19 57.2 ...
## $ predTe_ALL_U : num 34.3 23.6 49.7 22.9 34.9 ...
## $ Control_LambdaTu_U : num 1 1 1.03 1.16 1 ...
## $ Control_LambdaTe_U : num 1 1 1 1 0.799 ...
## $ Control_LambdaIntraTu_U: num 1 1 1.07 1.15 1 ...
## $ Control_LambdaIntraTe_U: num 1 1 1 1 0.873 ...
## $ Control_ALLTu_U : num 1 1 1.07 1.14 1.09 ...
## $ Control_ALLTe_U : num 1 1 1.447 0.969 1.017 ...Figure1
ggplot(subset(pred_coex1Gen_long, parameter=="predTe_onlyLambda" | parameter=="predTe_Lambda_INTRA" | parameter=="predTe_ALL"), aes(y=parameter3, x=value))+
facet_grid(.~Environment, labeller=labeller(Environment=Env))+
geom_errorbarh(aes(xmin=value_L, xmax=value_U, group=interaction(Te, Tu)), colour="black", height=0.5, position=position_dodge2(0.5))+
geom_point(aes(fill=interaction(Te, Tu)),size=2.5, position=position_dodge2(0.5), stat="identity", shape=21)+
geom_vline(xintercept = 1, colour="lightgray", linetype="dashed")+
theme_bw()+
theme_ines+
scale_fill_brewer(palette = "Spectral", labels=c("Te no cadmium:Tu no cadmium", "Te cadmium:Tu no cadmium", "Te no cadmium:Tu cadmium", "Te cadmium:Tu cadmium"), name="")+
guides(fill=guide_legend(nrow=2))+
xlab(expression(paste("Predicted offspring production for ", italic("T. evansi"))))+
scale_y_discrete(labels=c(expression(lambda+ alpha[ii] + alpha [ij]),expression(lambda+ alpha[ii]), expression(lambda)), limits=rev(levels(droplevels(subset(pred_coex1Gen_long, parameter=="predTe_onlyLambda" | parameter=="predTe_Lambda_INTRA" | parameter=="predTe_ALL"))$parameter3)))+
theme(legend.position = "bottom", axis.text = element_text(size=12), axis.title = element_text(face="plain", size=12))+
ylab("")
save_plot("./Analyses/cxr_normal_REP/Fig1A.pdf", width=17.5, height=10)
ggplot(subset(pred_coex1Gen_long, parameter=="predTu_onlyLambda" | parameter=="predTu_Lambda_INTRA" | parameter=="predTu_ALL"), aes(y=parameter3, x=value))+
facet_grid(.~Environment, labeller=labeller(Environment=Env))+
geom_errorbarh(aes(xmin=value_L, xmax=value_U, group=interaction(Te, Tu)), colour="black", height=0.5, position=position_dodge2(0.5))+
geom_point(aes(fill=interaction(Te, Tu)),size=2.5, position=position_dodge2(0.5), stat="identity", shape=21)+
geom_vline(xintercept = 1, colour="lightgray", linetype="dashed")+
theme_bw()+
theme_ines+
scale_fill_brewer(palette = "Spectral", labels=c("Te no cadmium:Tu no cadmium", "Te cadmium:Tu no cadmium", "Te no cadmium:Tu cadmium", "Te cadmium:Tu cadmium"), name="")+
xlab(expression(paste("Predicted offspring production for ", italic("T. urticae"))))+
guides(fill=guide_legend(nrow=2))+
scale_y_discrete(labels=c(expression(lambda+ alpha[ii] + alpha [ij]),expression(lambda+ alpha[ii]), expression(lambda)), limits=rev(levels(droplevels(subset(pred_coex1Gen_long, parameter=="predTu_onlyLambda" | parameter=="predTu_Lambda_INTRA" | parameter=="predTu_ALL"))$parameter3)))+
theme(legend.position = "bottom", axis.text = element_text(size=12), axis.title = element_text(face="plain", size=12))+
ylab("")
save_plot("./Analyses/cxr_normal_REP/Fig1B.pdf", width=17.5, height=10)6 - Bootstrap differences between selection regimes
For each question we will randomize the replicates between selection regimes.
6.1 - Does cadmium change parameters?
nboot<-1000
#Bootstrap to reestimate the p-value obtained for growth rate and intraspecific competition.
boot_tu_gr_intra_env<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" )
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Tu_lambda<-auxi[rand_numb,"Tu_lambda"] # randomizing the trais
auxi$Tu_intra<-auxi[rand_numb,"Tu_intra"]
auxi$Tu_inter<-auxi[rand_numb,"Tu_inter"]
gr <-glmmTMB(Tu_lambda~Environment, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Tu_intra~Environment, data=auxi)
inter<-glmmTMB(Tu_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Environment)%>% summarize(meanGr=mean(Tu_lambda, na.rm=TRUE), meanIntra=mean(Tu_intra, na.rm=TRUE), meaninter=mean(Tu_inter, na.rm=TRUE)) %>% as.data.frame()
# N - Cd
diff<-sum_auxi[2,c(2:4)]-sum_auxi[1,(2:4)]
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
c(gr_p, intra_p, inter_p, diff[1,1], diff[1,2], diff[1,3])
} )))
boot_te_gr_intra_env<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Tu_Regime=="SR1" & Te_Regime=="SR4" )
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Te_lambda<-auxi[rand_numb,"Te_lambda"] # randomizing the trais
auxi$Te_intra<-auxi[rand_numb,"Te_intra"]
auxi$Te_inter<-auxi[rand_numb,"Te_inter"]
gr <-glmmTMB(Te_lambda~Environment, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Te_intra~Environment, data=auxi)
inter <-glmmTMB(Te_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Environment)%>% summarize(meanGr=mean(Te_lambda, na.rm=TRUE), meanIntra=mean(Te_intra, na.rm=TRUE), meaninter=mean(Te_inter, na.rm=TRUE)) %>% as.data.frame()
# N - Cd
diff<-sum_auxi[2,c(2:4)]-sum_auxi[1,(2:4)]
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
c(gr_p, intra_p, inter_p,diff[1,1], diff[1,2], diff[1,3])
} )))
colnames(boot_tu_gr_intra_env)<-c("lambda_p","intra_p","inter_p", "lambda_diff", "intra_diff", "inter_diff")
colnames(boot_te_gr_intra_env)<-c("lambda_p","intra_p","inter_p", "lambda_diff", "intra_diff", "inter_diff")
str(boot_te_gr_intra_env)
ggplot(boot_tu_gr_intra_env, aes(x=lambda_p))+
geom_histogram()+
geom_vline(data=as.data.frame(Anova(gr_tu_cd_2)), aes_string(xintercept=as.data.frame(Anova(gr_tu_cd_2))[,3]))
print("Boot p-values for tests between environments")
length(which(boot_tu_gr_intra_env$lambda_p<=as.data.frame(Anova(gr_tu_cd_2))[,3]))/(nboot+1)
length(which(boot_tu_gr_intra_env$intra_p<=as.data.frame(Anova(intra_tu_cd_1))[,3]))/(nboot+1)
length(which(boot_tu_gr_intra_env$inter_p<=as.data.frame(Anova(inter_tu_cd_1))[,3]))/(nboot+1)
length(which(boot_te_gr_intra_env$lambda_p<=as.data.frame(Anova(gr_te_cd_2))[,3]))/(nboot+1)
length(which(boot_te_gr_intra_env$intra_p<=as.data.frame(Anova(intra_te_cd_1))[,3]))/(nboot+1)
length(which(boot_te_gr_intra_env$inter_p<=as.data.frame(Anova(inter_te_cd_1))[,3]))/(nboot+1)
as.data.frame(Anova(gr_tu_cd_2))[,3]
as.data.frame(Anova(intra_tu_cd_1))[,3]
as.data.frame(Anova(inter_tu_cd_1))[,3]
as.data.frame(Anova(gr_te_cd_2))[,3]
as.data.frame(Anova(intra_te_cd_1))[,3]
as.data.frame(Anova(inter_te_cd_1))[,3]
length(which(boot_tu_gr_intra_env$lambda_p<=0.05))/(nboot)
length(which(boot_tu_gr_intra_env$intra_p<=0.05))/(nboot)
length(which(boot_tu_gr_intra_env$inter_p<=0.05))/(nboot)
length(which(boot_te_gr_intra_env$lambda_p<=0.05))/(nboot)
length(which(boot_te_gr_intra_env$intra_p<=0.05))/(nboot)
length(which(boot_te_gr_intra_env$inter_p<=0.05))/(nboot)6.2 - Does evolution change the performance in cadmium?
#Bootstrap to reestimate the p-value obtained for growth rate and intraspecific competition.
boot_tu_evolcd<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Environment=="Cd"& Te_Regime=="SR4")
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Tu_lambda<-auxi[rand_numb,"Tu_lambda"] # randomizing the trais
auxi$Tu_intra<-auxi[rand_numb,"Tu_intra"]
auxi2<-subset(param_all_w0, Environment=="Cd")
rand_numb2<-sample(c(1:dim(auxi2)[1]), dim(auxi2)[1], replace = TRUE)
auxi2$Tu_inter<-auxi2[rand_numb2,"Tu_inter"]
gr <-glmmTMB(Tu_lambda~Tu_Regime, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Tu_intra~Tu_Regime, data=auxi)
inter<-glmmTMB(Tu_inter~Tu_Regime*Te_Regime, data=auxi2)
#inter<-glmmTMB(Tu_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Tu_Regime)%>% summarize(meanGr=mean(Tu_lambda, na.rm=TRUE), meanIntra=mean(Tu_intra, na.rm=TRUE)) %>% as.data.frame()
sum_auxi2<-auxi2 %>% group_by(Tu_Regime, Te_Regime)%>% summarize( meanInter=mean(Tu_inter, na.rm=TRUE)) %>% as.data.frame()
# N - Cd
diff<-sum_auxi[2,c(2:3)]-sum_auxi[1,(2:3)]
diff2<-sum_auxi2[1,3]-sum_auxi2[2,3] # Evolution of the competitor with control focal
diff3<-sum_auxi2[1,3]-sum_auxi2[3,3] # Evolution of the focal with control competitor
diff4<-sum_auxi2[2,3]-sum_auxi2[4,3] # Evolution of the focal with evolved competitor
diff5<-sum_auxi2[3,3]-sum_auxi2[4,3] # Evolution of the competitor with evolved focal
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
inter_p2<-as.data.frame(Anova(inter))[2,3]
inter_p3<-as.data.frame(Anova(inter))[3,3]
c(gr_p, intra_p, inter_p,inter_p2, inter_p3, diff[1,1], diff[1,2], diff2, diff3, diff4,diff5)
} )))
boot_te_evolcd<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Environment=="Cd"& Tu_Regime=="SR1")
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Te_lambda<-auxi[rand_numb,"Te_lambda"] # randomizing the trais
auxi$Te_intra<-auxi[rand_numb,"Te_intra"]
# print(x)
auxi2<-subset(param_all_w0, Environment=="Cd")
rand_numb2<-sample(c(1:dim(auxi2)[1]), dim(auxi2)[1], replace = TRUE)
auxi2$Te_inter<-auxi2[rand_numb2,"Te_inter"]
gr <-glmmTMB(Te_lambda~Te_Regime, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Te_intra~Te_Regime, data=auxi)
inter<-glmmTMB(Te_inter~Tu_Regime*Te_Regime, data=auxi2)
#inter<-glmmTMB(Tu_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Te_Regime)%>% summarize(meanGr=mean(Te_lambda, na.rm=TRUE), meanIntra=mean(Te_intra, na.rm=TRUE)) %>% as.data.frame()
sum_auxi2<-auxi2 %>% group_by(Tu_Regime, Te_Regime)%>% summarize( meanInter=mean(Te_inter, na.rm=TRUE)) %>% as.data.frame()
# N - Cd
diff<-sum_auxi[2,c(2:3)]-sum_auxi[1,(2:3)]
diff2<-sum_auxi2[1,3]-sum_auxi2[2,3] # Evolution of the competitor with control focal
diff3<-sum_auxi2[1,3]-sum_auxi2[3,3] # Evolution of the focal with control competitor
diff4<-sum_auxi2[2,3]-sum_auxi2[4,3] # Evolution of the focal with evolved competitor
diff5<-sum_auxi2[3,3]-sum_auxi2[4,3] # Evolution of the competitor with evolved focal
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
inter_p2<-as.data.frame(Anova(inter))[2,3]
inter_p3<-as.data.frame(Anova(inter))[3,3]
c(gr_p, intra_p, inter_p,inter_p2, inter_p3, diff[1,1], diff[1,2], diff2, diff3, diff4,diff5)
} )))
colnames(boot_tu_evolcd)<-c("lambda_p","intra_p","inter_p_TuReg","inter_p_TeReg","inter_p_int", "lambda_diff", "intra_diff", "inter_diffEvolComp_focalControl","inter_diffEvolFocal_CompControl","inter_diffEvolFocal_EvolComp","inter_diffEvolComp_focalEvol" )
colnames(boot_te_evolcd)<-c("lambda_p","intra_p","inter_p_TuReg","inter_p_TeReg","inter_p_int", "lambda_diff", "intra_diff", "inter_diffEvolComp_focalControl","inter_diffEvolFocal_CompControl","inter_diffEvolFocal_EvolComp","inter_diffEvolComp_focalEvol" )
print("Boot p-values for tests for evolution in cadmium")
length(which(boot_tu_evolcd$lambda_p<=as.data.frame(Anova(gr_tu_ev_2))[,3]))/(nboot+1)
length(which(boot_tu_evolcd$intra_p<=as.data.frame(Anova(intra_tu_ev_1))[,3]))/(nboot+1)
length(which(boot_tu_evolcd$inter_p_TuReg<=as.data.frame(Anova(inter_tu_ev_1))[1,3]))/(nboot+1)
length(which(boot_tu_evolcd$inter_p_TeReg<=as.data.frame(Anova(inter_tu_ev_1))[2,3]))/(nboot+1)
length(which(boot_tu_evolcd$inter_p_int<=as.data.frame(Anova(inter_tu_ev_1))[3,3]))/(nboot+1)
length(which(boot_te_evolcd$lambda_p<=as.data.frame(Anova(gr_te_ev_2))[,3]))/(nboot+1)
length(which(boot_te_evolcd$intra_p<=as.data.frame(Anova(intra_te_ev_1))[,3]))/(nboot+1)
length(which(boot_te_evolcd$inter_p_TuReg<=as.data.frame(Anova(inter_te_ev_1))[1,3]))/(nboot+1)
length(which(boot_te_evolcd$inter_p_TeReg<=as.data.frame(Anova(inter_te_ev_1))[2,3]))/(nboot+1)
length(which(boot_te_evolcd$inter_p_int<=as.data.frame(Anova(inter_te_ev_1))[3,3]))/(nboot+1)
hist(boot_tu_evolcd$lambda_p)
abline(v=as.data.frame(Anova(gr_tu_ev_2))[,3], col="red")
hist(boot_tu_evolcd$intra_p)
abline(v=as.data.frame(Anova(intra_tu_ev_1))[,3], col="red")
hist(boot_tu_evolcd$inter_p_TuReg)
abline(v=as.data.frame(Anova(inter_tu_ev_1))[1,3], col="red")
hist(boot_tu_evolcd$inter_p_TeReg)
abline(v=as.data.frame(Anova(inter_tu_ev_1))[2,3], col="red")
hist(boot_tu_evolcd$inter_p_int)
abline(v=as.data.frame(Anova(inter_tu_ev_1))[3,3], col="red")
hist(boot_te_evolcd$lambda_p)
abline(v=as.data.frame(Anova(gr_te_ev_2))[,3], col="red")
hist(boot_te_evolcd$intra_p)
abline(v=as.data.frame(Anova(intra_te_ev_1))[,3], col="red")
hist(boot_te_evolcd$inter_p_TuReg)
abline(v=as.data.frame(Anova(inter_te_ev_1))[1,3], col="red")
hist(boot_te_evolcd$inter_p_TeReg)
abline(v=as.data.frame(Anova(inter_te_ev_1))[2,3], col="red")
hist(boot_te_evolcd$inter_p_int)
abline(v=as.data.frame(Anova(inter_te_ev_1))[3,3], col="red")
as.data.frame(Anova(gr_tu_ev_2))[,3]
as.data.frame(Anova(intra_tu_ev_1))[,3]
as.data.frame(Anova(inter_tu_ev_1))[1,3]
as.data.frame(Anova(inter_tu_ev_1))[2,3]
as.data.frame(Anova(inter_tu_ev_1))[3,3]
as.data.frame(Anova(gr_te_ev_2))[,3]
as.data.frame(Anova(intra_te_ev_1))[,3]
as.data.frame(Anova(inter_te_ev_1))[1,3]
as.data.frame(Anova(inter_te_ev_1))[2,3]
as.data.frame(Anova(inter_te_ev_1))[3,3]
#Number of times that p-value was lower than 0.05
length(which(boot_tu_evolcd$lambda_p<=0.05))/(nboot)
length(which(boot_tu_evolcd$intra_p<=0.05))/(nboot)
length(which(boot_tu_evolcd$inter_p_TuReg<=0.05))/(nboot)
length(which(boot_tu_evolcd$inter_p_TeReg<=0.05))/(nboot)
length(which(boot_tu_evolcd$inter_p_int<=0.05))/(nboot)
length(which(boot_te_evolcd$lambda_p<=0.05))/(nboot)
length(which(boot_te_evolcd$intra_p<=0.05))/(nboot)
length(which(boot_te_evolcd$inter_p_TuReg<=0.05))/(nboot)
length(which(boot_te_evolcd$inter_p_TeReg<=0.05))/(nboot)
length(which(boot_te_evolcd$inter_p_int<=0.05))/(nboot)2.3.4 - Does evolution change the ancestral?
#Bootstrap to reestimate the p-value obtained for growth rate and intraspecific competition.
boot_tu_evolN<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Environment=="N"& Te_Regime=="SR4")
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Tu_lambda<-auxi[rand_numb,"Tu_lambda"] # randomizing the trais
auxi$Tu_intra<-auxi[rand_numb,"Tu_intra"]
auxi2<-subset(param_all_w0, Environment=="N")
rand_numb2<-sample(c(1:dim(auxi2)[1]), dim(auxi2)[1], replace = TRUE)
auxi2$Tu_inter<-auxi2[rand_numb2,"Tu_inter"]
gr <-glmmTMB(Tu_lambda~Tu_Regime, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Tu_intra~Tu_Regime, data=auxi)
inter<-glmmTMB(Tu_inter~Tu_Regime*Te_Regime, data=auxi2)
#inter<-glmmTMB(Tu_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Tu_Regime)%>% summarize(meanGr=mean(Tu_lambda, na.rm=TRUE), meanIntra=mean(Tu_intra, na.rm=TRUE)) %>% as.data.frame()
sum_auxi2<-auxi2 %>% group_by(Tu_Regime, Te_Regime)%>% summarize( meanInter=mean(Tu_inter, na.rm=TRUE)) %>% as.data.frame()
# N - N
diff<-sum_auxi[2,c(2:3)]-sum_auxi[1,(2:3)]
diff2<-sum_auxi2[1,3]-sum_auxi2[2,3] # Evolution of the competitor with control focal
diff3<-sum_auxi2[1,3]-sum_auxi2[3,3] # Evolution of the focal with control competitor
diff4<-sum_auxi2[2,3]-sum_auxi2[4,3] # Evolution of the focal with evolved competitor
diff5<-sum_auxi2[3,3]-sum_auxi2[4,3] # Evolution of the competitor with evolved focal
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
inter_p2<-as.data.frame(Anova(inter))[2,3]
inter_p3<-as.data.frame(Anova(inter))[3,3]
c(gr_p, intra_p, inter_p,inter_p2, inter_p3, diff[1,1], diff[1,2], diff2, diff3, diff4,diff5)
} )))
boot_te_evolN<-as.data.frame(t(sapply(c(1:nboot),function(x){
if(x%%10 ==0){
print(x)
}
auxi<-subset(param_all_w0, Environment=="N"& Tu_Regime=="SR1")
rand_numb<-sample(c(1:dim(auxi)[1]), dim(auxi)[1], replace = TRUE)
auxi$Te_lambda<-auxi[rand_numb,"Te_lambda"] # randomizing the trais
auxi$Te_intra<-auxi[rand_numb,"Te_intra"]
# print(x)
auxi2<-subset(param_all_w0, Environment=="N")
rand_numb2<-sample(c(1:dim(auxi2)[1]), dim(auxi2)[1], replace = TRUE)
auxi2$Te_inter<-auxi2[rand_numb2,"Te_inter"]
gr <-glmmTMB(Te_lambda~Te_Regime, data=auxi, family=Gamma(link="log"))
intra <-glmmTMB(Te_intra~Te_Regime, data=auxi)
inter<-glmmTMB(Te_inter~Tu_Regime*Te_Regime, data=auxi2)
#inter<-glmmTMB(Tu_inter~Environment, data=auxi)
sum_auxi<-auxi %>% group_by(Te_Regime)%>% summarize(meanGr=mean(Te_lambda, na.rm=TRUE), meanIntra=mean(Te_intra, na.rm=TRUE)) %>% as.data.frame()
sum_auxi2<-auxi2 %>% group_by(Tu_Regime, Te_Regime)%>% summarize( meanInter=mean(Te_inter, na.rm=TRUE)) %>% as.data.frame()
# N - N
diff<-sum_auxi[2,c(2:3)]-sum_auxi[1,(2:3)]
diff2<-sum_auxi2[1,3]-sum_auxi2[2,3] # Evolution of the competitor with control focal
diff3<-sum_auxi2[1,3]-sum_auxi2[3,3] # Evolution of the focal with control competitor
diff4<-sum_auxi2[2,3]-sum_auxi2[4,3] # Evolution of the focal with evolved competitor
diff5<-sum_auxi2[3,3]-sum_auxi2[4,3] # Evolution of the competitor with evolved focal
gr_p<-as.data.frame(Anova(gr))[1,3]
intra_p<-as.data.frame(Anova(intra))[1,3]
inter_p<-as.data.frame(Anova(inter))[1,3]
inter_p2<-as.data.frame(Anova(inter))[2,3]
inter_p3<-as.data.frame(Anova(inter))[3,3]
c(gr_p, intra_p, inter_p,inter_p2, inter_p3, diff[1,1], diff[1,2], diff2, diff3, diff4,diff5)
} )))
colnames(boot_tu_evolN)<-c("lambda_p","intra_p","inter_p_TuReg","inter_p_TeReg","inter_p_int", "lambda_diff", "intra_diff", "inter_diffEvolComp_focalControl","inter_diffEvolFocal_CompControl","inter_diffEvolFocal_EvolComp","inter_diffEvolComp_focalEvol" )
colnames(boot_te_evolN)<-c("lambda_p","intra_p","inter_p_TuReg","inter_p_TeReg","inter_p_int", "lambda_diff", "intra_diff", "inter_diffEvolComp_focalControl","inter_diffEvolFocal_CompControl","inter_diffEvolFocal_EvolComp","inter_diffEvolComp_focalEvol" )
print("Boot p-values for tests for evolution in cadmium")
length(which(boot_tu_evolN$lambda_p<=as.data.frame(Anova(gr_tu_an_2))[,3]))/(nboot+1)
length(which(boot_tu_evolN$intra_p<=as.data.frame(Anova(intra_tu_an_1))[,3]))/(nboot+1)
length(which(boot_tu_evolN$inter_p_TuReg<=as.data.frame(Anova(inter_tu_an_1))[1,3]))/(nboot+1)
length(which(boot_tu_evolN$inter_p_TeReg<=as.data.frame(Anova(inter_tu_an_1))[2,3]))/(nboot+1)
length(which(boot_tu_evolN$inter_p_int<=as.data.frame(Anova(inter_tu_an_1))[3,3]))/(nboot+1)
length(which(boot_te_evolN$lambda_p<=as.data.frame(Anova(gr_te_an_2))[,3]))/(nboot+1)
length(which(boot_te_evolN$intra_p<=as.data.frame(Anova(intra_te_an_1))[,3]))/(nboot+1)
length(which(boot_te_evolN$inter_p_TuReg<=as.data.frame(Anova(inter_te_an_1))[1,3]))/(nboot+1)
length(which(boot_te_evolN$inter_p_TeReg<=as.data.frame(Anova(inter_te_an_1))[2,3]))/(nboot+1)
length(which(boot_te_evolN$inter_p_int<=as.data.frame(Anova(inter_te_an_1))[3,3]))/(nboot+1)
hist(boot_tu_evolN$lambda_p)
abline(v=as.data.frame(Anova(gr_tu_an_2))[,3], col="red")
hist(boot_tu_evolN$intra_p)
abline(v=as.data.frame(Anova(intra_tu_an_1))[,3], col="red")
hist(boot_tu_evolN$inter_p_TuReg)
abline(v=as.data.frame(Anova(inter_tu_an_1))[1,3], col="red")
hist(boot_tu_evolN$inter_p_TeReg)
abline(v=as.data.frame(Anova(inter_tu_an_1))[2,3], col="red")
hist(boot_tu_evolN$inter_p_int)
abline(v=as.data.frame(Anova(inter_tu_an_1))[3,3], col="red")
hist(boot_te_evolN$lambda_p)
abline(v=as.data.frame(Anova(gr_te_an_2))[,3], col="red")
hist(boot_te_evolN$intra_p)
abline(v=as.data.frame(Anova(intra_te_an_1))[,3], col="red")
hist(boot_te_evolN$inter_p_TuReg)
abline(v=as.data.frame(Anova(inter_te_an_1))[1,3], col="red")
hist(boot_te_evolN$inter_p_TeReg)
abline(v=as.data.frame(Anova(inter_te_an_1))[2,3], col="red")
hist(boot_te_evolN$inter_p_int)
abline(v=as.data.frame(Anova(inter_te_an_1))[3,3], col="red")
as.data.frame(Anova(gr_tu_an_2))[,3]
as.data.frame(Anova(intra_tu_an_1))[,3]
as.data.frame(Anova(inter_tu_an_1))[1,3]
as.data.frame(Anova(inter_tu_an_1))[2,3]
as.data.frame(Anova(inter_tu_an_1))[3,3]
as.data.frame(Anova(gr_te_an_2))[,3]
as.data.frame(Anova(intra_te_an_1))[,3]
as.data.frame(Anova(inter_te_an_1))[1,3]
as.data.frame(Anova(inter_te_an_1))[2,3]
as.data.frame(Anova(inter_te_an_1))[3,3]
length(which(boot_tu_evolN$lambda_p<=0.05))/(nboot)
length(which(boot_tu_evolN$intra_p<=0.05))/(nboot)
length(which(boot_tu_evolN$inter_p_TuReg<=0.05))/(nboot)
length(which(boot_tu_evolN$inter_p_TeReg<=0.05))/(nboot)
length(which(boot_tu_evolN$inter_p_int<=0.05))/(nboot)
length(which(boot_te_evolN$lambda_p<=0.05))/(nboot)
length(which(boot_te_evolN$intra_p<=0.05))/(nboot)
length(which(boot_te_evolN$inter_p_TuReg<=0.05))/(nboot)
length(which(boot_te_evolN$inter_p_TeReg<=0.05))/(nboot)
length(which(boot_te_evolN$inter_p_int<=0.05))/(nboot)